Keywords: Safety
Abstract: As the aviation community including companies, regulators, researchers etc., and society at large contemplates the integration of highly automated unmanned aircraft into the airspace currently utilized by manned aviation, several important topics arise. One key consideration is safety of the automation systems and subsystems both onboard the unmanned aircraft, onboard manned aircraft and on the ground. Consider the context added by recent high profile commercial aviation accidents such as the San Francisco Asiana Airlines accident in 2013 and the Atlantic Ocean Air France flight 447 accident in 2009. Both of these accidents involved manned aircraft with high levels of automation onboard. Many opinions, studies and reports delivered against this backdrop could be construed to imply that the means of improving safety lies in reducing levels of automation and autonomy in the various functions performed by computers, software, algorithms etc. and returning to more human-in-the-loop architectures. The unmanned systems community has blossomed in the past two decades in no small measure due to the proliferation and widespread availability of high performance, low cost and low power consumption computation, sensing, communication, imaging and other technologies. This has resulted in an unprecedented level of automation and autonomy capability being deployed on UAS systems ranging from very small to very large and covering a plethora of roles and missions. This presentation considers the current state and this very exciting future to highlight domains in which much technological development is needed. This include systems engineering, software development, redundancy management, control and estimation theory, requirements development and tracking, as well as concomitant design, development and testing techniques, tools and methodologies in order to leverage the enormous value brought by highly automated Safety Critical Systems. A fundamentally important attribute of these technologies is the need for enabling development and full scale deployment of Safety Critical Systems at a market-affordable cost and price. Such solutions will provide enormous technological and business opportunities to the Control Systems Community by enabling a vision of delivering Safety Critical Systems that far exceed the reliability of such systems today whilst being much more affordable on multiple facets. The market needs and opportunities expand far beyond aviation, to include all forms of transportation, medical devices and systems, power generation systems and more.

Biography: Dave Vos was born in Paarl, South Africa in 1961 and grew up in the Cape Town region He holds engineering degrees Hons. B.Ing (Aero) from the University of Stellenbosch (1983) and S.M. (1989) and Ph.D. (1992) from MIT’s Aero/Astro Department, Boston, USA. His graduate work at MIT demonstrated the world’s first autonomous unicycle robot. Dave was CEO, CTO and Founder of Athena Technologies, incorporated in 1998. Athena developed and produced navigation, guidance, flight and engine control systems for the Unmanned Aircraft market. Athena grew rapidly to become a market leader delivering autonomous navigation and control systems for a wide variety of unmanned aircraft programs worldwide and was acquired by Rockwell Collins in 2008. Vos was one of Ernst and Young Entrepreneur of the Year Winners in the Washington DC Region in 2007 and holds several patents in nonlinear control systems, failure detection systems, optimal power control systems and more. He recently served on the USA FAA Aviation Rulemaking Committee for Integration of Unmanned Aircraft into the National Airspace System as well as on the NASA Unmanned Systems Advisory Committee. Current areas of business and technology interest include Aerospace, Transportation and Renewable Energy.

Keywords: Modeling and simulation of power systems
Abstract: With the increasing scale of renewable generations and uncertain loads that connected to power systems, the uncertainties of power injections in both source side and load side have become significant, due to their randomness and intermittency. How to model the uncertainties with an appropriate method and analyse the influence of uncertainties on power flow has become the basis of power system planning and operation. This paper proposes interval models for wind power and electric vehicles, and then applies an interval arithmetic-based algorithm to analyse the influence on power flow caused by the uncertainties. IEEE 30-bus test system with integration of wind power and electric vehicles is used as a simulation example to analyse the influence in details.

Keywords: Control of renewable energy resources, Smart grids, Optimal operation and control of power systems
Abstract: Wind power is inherently variable and intermittent, and thus it brings great challenges to the operation of power systems. Noting that there have been considerable dispatchable sources in power systems, the coordination of wind power generation with the dispatchable sources provides the possibility to overcome the drawbacks of wind power. In this paper, the choice of coordination objective is discussed, or the output of wind power or the combine system should satisfy the requirements of the whole power systems. Based on the above principle, two examples are given. Firstly, the coordinated optimal control of wind-storage system is proposed. The objective of the coordinated control is to meet the ramp rate limit of grid code. Secondly, the coordinated optimal dispatch of wind-thermal-pumped hydro system is proposed. The objective of the coordinated dispatch is to make the output of the combined system more correlated with the system load. The optimal model and algorithm of the two examples are designed. The simulation results also validate the effectiveness of the proposed methods.

Keywords: Control of renewable energy resources, Smart grids, Optimal operation and control of power systems
Abstract: The high penetration of renewable generation raises great concerns in grid operation on whether system will have sufficient flexibility for balancing the intermittent variation of renewable generation. With the development of smart grid, technologies like demand response, distribution generation, electric vehicle and energy storage become more popular. It is foreseen that with the increasing participation of these flexible load resources and high penetration of renewable generation, the system operations of the future interconnected power grid will become more complicated. This paper discusses the system operational challenges, analyzes the potentials of load flexibility, and then proposes a multi-agent system control framework to support the interactive operations of "Source-Grid-Load". An interactive leading control process is introduced to lead the flexible resources ramping with a sequence of advisory dispatches, reduce the balancing needs from the frequency control generators.

Keywords: Control of renewable energy resources, Analysis and control in deregulated power systems, Optimal operation and control of power systems
Abstract: It is necessary for power systems to provide flexible power to balance the fluctuation and intermittency of wind power. For the large-scale and central-integrated wind generation (such as that in China), the required flexible power may be very significant and expensive. In market environment, it is important to design a proper market mechanism and price signal which can promote conventional power and load demand to integrate wind power and gain corresponding profit. Firstly, the paper analyzes the current economy and dispatch policy for wind power in China. Then, a Block Bidding mode for wind power is presented. Unlike Timely Bidding mode that power is auctioned hourly by hourly, for Block Bidding the wind power energy is divided into different energy block. And these blocks are auctioned by conventional power resources and load demands. The Block Bidding mode has some advantages: 1) subdivides wind power by energy quality, and then the value of different parts can be revealed; 2) provides incentive for conventional power resource and load demand to integrate wind power; 3) is convenient for trading and dispatching wind power. Finally, a simple numerical case is given to illustrate the validity of Block Bidding mode.

Keywords: Control of renewable energy resources, Optimal operation and control of power systems, Real time simulation and dispatching
Abstract: High penetration of wind power brings a great challenge to the power system scheduling mode and technologies. Existing researches show that load resources would have great potential and economic benefits on this aspect. However, it will cause a complex control problem when a large number of loads participate in active power scheduling. Therefore, How to establish an effective and feasible load scheduling model is necessary. Based on the multi-agent technology, the interaction mechanism between demand response loads participate in the dispatching operation and the specific load dispatching model are studied in this paper. First, the 3-layered architecture of ‘control centre’s decision-making layer, agent-coordination layer, local load response layer’ is established, and the working mechanism of load agents is designed; Then, the decision-making model of the load agent is established, and a bidding strategy for the global optimum is put forward to the power company based on the game theory; By analysing the scheduling cost of different adjustable resources which can be generators or loads, the power company will make decisions for minimizing their scheduling costs; On this basis, the transaction process between the load agents and the dispatching centre is designed. Finally, examples on the IEEE 39-bus model demonstrate the validity of the model and strategy proposed in this paper. The results show that, with the multi-agent load dispatching mechanism, the load resources can be adequately utilized and the generator reserve is decreased obviously, the cost of dispatching system is reduced effectively, and a win-win status of the power company and load agents is achieved.

Keywords: Smart grids, Intelligent control of power systems, Modeling and simulation of power systems
Abstract: In order to meet the challenges in the future smart grid operations, this paper presents a new concept of Source-Grid-Load interactive operation and control, which provides new ideas and effective technical means for the future smart grid operations. The Source-Grid-Load interactive operation consists of complementarities of power supply sources, coordination between power supply sources and power grid, interactions between grid and load, as well as interactions between power supply sources and system load. It aims to improve energy balance and dynamic power balancing ability among the power supply sources, load and power grid more economically, efficiently and safely. Based on analyses of the complex interaction characteristics of the future grid, this paper proposes a theory and technology framework for the interactive and flexible system operation, puts forward that in-depth study shall be made to understand the Source-Grid-Load complex interaction characteristics, form the interaction modeling method, develop the technologies of energy balance and security analysis, and break through the technology of the Source-Grid-Load coordinative control in the environment of wide area complex interaction. It provides systematic basis for the flexible and interactive operation and control in the future smart grid, and helps to push the development of power system scheduling from the unidirectional mode ‘generation following the load’ to the smart and interactive mode of Source-Grid-Load.

Keywords: Nonlinear observers and filter design, Lyapunov methods, Stability of nonlinear systems
Abstract: As rechargeable lithium-ion batteries are widely used in many applications nowadays, how to accurately evaluate the battery's state of charge(SOC) becomes a more and more important issue. A new method for estimating the SOC of lithium-ion batteries based on an inclusive equivalent circuit model is proposed in this paper. To the best of our knowledge, the parameters in the model are usually considered as constants to simplify the problem of the SOC estimation, which may lead to some estimation error. In order to get more accurate estimation results, the capacitances and resistances in the battery model are considered as nonlinear functions of the SOC and the temperature of the battery. The resistances also depend on the battery's charging or discharging mode. Nonlinear relationship between the open circuit voltage(OCV) and the SOC is considered and a nonlinear observer is designed to estimate the inner characteristics of the battery. Lyapunov stability analysis is utilized to prove its performance and simulation results are provided to illustrate the performance of the proposed approach.

Keywords: Nonlinear observers and filter design, Relaxations
Abstract: This paper presents a set-based output energy measure for constrained polynomial systems with parameter uncertainties. Output energy is measured in terms of the L2-norm on a finite-time interval while the initial conditions and parameters are allowed to take values from a set. By specifying a bound on the output norm, the measure allows further to determine the set of initial conditions and parameters which lead to satisfaction of this bound. Furthermore, this set characterizes whether an uncertain system can be estimated by a norm-observer and, therefore, can be applied for observability analysis. The derivation of the set is based on recasting a nonlinear program with embedded differential equations into an infinite-dimensional linear program. This is achieved by reformulating the system dynamics in terms of occupation measures. The chosen relaxation approach of the linear program generically guarantees that the obtained outer-approximation converges, for increasing relaxation order, to the true set of initial conditions and parameters satisfying the specified bound on the output norm.

Keywords: Nonlinear observers and filter design, Sliding mode control
Abstract: In this work, two high order sliding mode observers under cascade form are proposed in order to estimate the important states and inputs affecting the lateral dynamics of two-wheeled vehicles. The first observer is based on a vision system and is used to estimate the lateral velocity of the vehicle. This velocity is considered as an additional measure for the second observer which is designed to estimate the tire forces, the roll angle and torque applied to the handle-bar.

The main contribution of this work is the estimation of the tire forces even with the variation of the longitudinal velocity and without the prior knowledge of the pneumatic coefficients.

Keywords: Nonlinear observers and filter design, Networked systems, Robust estimation
Abstract: In this paper a method for the design of state observers for a class of complex network systems with nonlinear dynamics is presented. Necessary conditions on the dynamics of the nodes are given, i.e. how the interconnection enters and which states have to be measured, such that the arbitrary complex interconnection can be dealt with as an unknown input. Sufficient existence conditions for an Unknown Input Observer (UIO) for this class of systems are stated in terms of an LMI representing the dissipativity properties of the system. The design method is applied to monitor an epidemic disease in a complex network.

Keywords: Nonlinear observers and filter design, Output feedback control, Tracking
Abstract: Underactuated mechanical systems are systems with less actuators than degrees of freedom. Therefore, it is complicated to measure all states, i.e. angular positions or angular velocities, of the mechanical system. Alternative solution consists in an observer design such that unmeasurable states are estimated. For this purpose, a high gain observer for an Acrobot was introduced. By virtue of an Acrobot embedding into a 4-link model, the high gain observer was simply extended and applied to the 4-link model. The main aim of this paper consists in a coupling of a method of the Acrobot embedding into the 4-link model and the high gain observer design for the Acrobot. The coupling results in an observer for the 4-link model with the same structure as the already developed high gain observer for the Acrobot model. The control of the 4-link model using the embedding method is based on a definition of constraining functions for knees control whereas the remaining angle in the hip is controlled in the same way as it would be an Acrobot's angle. The ability of feedback tracking of the walking-like trajectory of the 4-link with observed geometry during a swing phase of a single step is demonstrated in simulations.

Keywords: Nonlinear observers and filter design, Parameter-varying systems, Input-to-State Stability
Abstract: A nonlinear observer that estimates well flow rates and downhole pressure based on topside measurements only has been presented in the literature. As demonstrated in laboratory experiments the observer is suitable for a coupling with conventional PI control for stabilization of slug flow. The design of the observer is based on a nonlinear model with a linear inflow relation, and demands knowledge of several uncertain well parameters, including the reservoir pressure and production index. In this paper we present an adaptive extension to the nonlinear observer that eliminates one uncertain parameter by estimating the reservoir pressure. The extended observer is shown to be globally uniformly asymptotically stable and retain the properties of the original observer.

Keywords: Closed loop identification, Fault detection and diagnosis, Filtering and smoothing
Abstract: A sequential method for Control Performance Diagnosis using a classication tree to predict possible root-causes of poor performance is presented. The classication tree methodology is used to combine process pre-assessment (nonlinearities detection, delays estimation and controller assessment), Control Performance Assessment (CPA) and ANalysis Of VAriance (ANOVA) into an integrated framework. A initial process data set is analysed and the results are used as decision thresholds for the classication tree. The methodology is capable to identify root-causes such as:poor tuning, inadequate control structure, nonlinearities, process mismatch and disturbance changes. The proposed methodology is applied to individual loops of a tandem cold rolling mill.

Keywords: Closed loop identification, Identification for control, Statistical data analysis
Abstract: In industry, system identification is a time-consuming exercise that impacts the profitability and safety of the plant. One way to avoid this problem is to use stored historical process data in estimating the required process models. Given the large amount of data available, which is often corrupted due to process disruptions, loss of information, and poor data quality, automated segmentation of the data set would be an invaluable asset. Recently, two different methods have been proposed to accomplish this task: one based on Laguerre models and one based on autoregressive with exogenous input (ARX) models. In this paper, the Laguerre approach will be analysed and it will be shown that the results are dependent on selecting appropriate Laguerre model parameters and input signals, while relatively insensitive to the variance thresholds. Furthermore, this approach has a tendency to overpartition the data set based on the smallest changes in the process. Therefore, in order to decrease the number of models identified it is proposed to couple this method with an entropy-based metric for determining similar models. Based on simulations that include this entropy metric, it is shown that a reduction in model partitions is obtained.

Keywords: Closed loop identification
Abstract: With advancing technology, systems are becoming increasingly interconnected and form more complex networks. Additionally, more measurements are available from systems due to cheaper sensors. Hence there is a need for identication methods specifically designed for networks. For dynamic networks with known interconnection structures, several methods have been proposed for obtaining consistent estimates. We suppose that the internal variables in the network are measured with noise, but that there are external reference signals present in the network that are known exactly. A method that is able to deal with this situation in the two stage method, which solves several open loop identication problems sequentially. In this paper it is shown that solving the problems simultaneously leads to estimates with lower variance.

Keywords: Closed loop identification, Identification for control, Input and excitation design
Abstract: In recent years, advanced control techniques such as Model Predictive Control based on optimization and making use of a model providing the predictions of the future behavior of the controlled system have been massively developed. These model-based controllers rely heavily on the accuracy of the available model (predictor of the controlled system behavior) which is crucial for their proper functioning. However, as the current operating conditions can be shifted away from those under which the model has been identified, the model sometimes happens to lose its prediction properties and needs to be re-identified. Unlike the theoretical assumptions, the data from the real operation suffer from undesired phenomena accompanying the closed-loop data. In the current paper, we focus on developing an algorithm which would serve as an alternative to the (often costly or even unrealizable) open loop excitation experiment. The requirements such an algorithm should meet are: low computational complexity, low level of original MPC performance degradation and ability to provide sufficiently informative data when necessary. Unlike to the currently available approaches which solve this problem for the classical MPC formulation (tracking error penalization), in this paper we propose an algorithm which works well also for the zone MPC formulation (penalization of output zone violation), however, it is versatile enough and can be extended considering wider variety of the optimization formulations.

Keywords: Distributed control and estimation, Control under communication constraints, Multi-agent systems
Abstract: This paper presents an explicit solution to a two player distributed LQR problem in which communication between controllers occurs across a communication link with varying delay. We extend known dynamic programming methods to accommodate this varying delay, and show that under suitable assumptions, the optimal control actions are linear in their information, and that the resulting controller has piecewise linear dynamics dictated by the current effective delay regime.

Keywords: Distributed control and estimation, Linear parametrically varying (LPV) methodologies
Abstract: Recently, distributed controller synthesis approaches for decomposable systems, a subclass of distributed systems with identical subsystems, where the interconnection can be described as LFT interconnection, have been proposed. In order to make these approaches tractable for systems containing a very large number of subsystems, constraints on the Lyapunov matrix and the multiplier matrices are introduced that render the complexity of analysis and controller synthesis smaller and in best case independent on the number of subsystems. Those assumptions have to be paid for with conservatism, which is investigated in this work. It is proven that the conservatism is not reduced if either only the Lyapunov or only the multiplier matrices are constrained, when compared with having constraints on both simultaneously.

Keywords: Multi-agent systems, Distributed control and estimation, Complex system management
Abstract: Scalability is a fundamental requirement in control design for large-scale systems. Typically, it needs to be considered explicitly at the expense of performance degradation and more complicated design procedures. In this paper, we present a class of large scale systems where scalability is an inherent property of the optimal centralized solution. More specifically, we study a coordination problem where a group of identical subsystems are required to satisfy an equality constraint on the sum of their inputs. We show that the problem can be completely decomposed in terms of the unconstrained problems associated with each subsystem. In particular, the computational effort required to obtain the optimal solution is independent of the number of subsystems and the only global information processing required to execute the optimal control law is a simple summation, which scales well when the number of subsystems grows large.

Keywords: Distributed control and estimation, Control over networks, Multi-agent systems
Abstract: This paper considers the distributed estimation of an unstable target via constant-gain estimators under local communications and channel fading. The communication graph is assumed to be fixed and undirected, and the channel fading is assumed to be identical. Necessary and sufficient conditions on communication network over which the state of the unstable target can be estimated in the mean square sense are given for both continuous-time and discrete-time cases, which reveal the fundamental limitation on distributed estimation induced by local communications, channel fading, and target dynamics. In addition, our results for the case without channel fading and the case with separate communications are consistent with the results in the literature.

Keywords: Multi-agent systems, Coordination of multiple vehicle systems, Robust adaptive control
Abstract: The robust output feedback consensus problem for homogeneous networked Negative-Imaginary (NI) systems is investigated in this paper. By virtue of the NI systems stability theorem, a set of reasonable yet elegant conditions are derived for guaranteeing consensus under L2 disturbances and NI model uncertainty. Furthermore, the eventual convergence sets of several special NI systems that are commonly studied in the literature are also presented. It is shown how the results in this work embed and generalise earlier results to these classes of systems. Numerical examples are given to illustrate the results of this paper.

Keywords: Fractional systems
Abstract: In this paper, it is shown that linear time-invariant fractional models do not reflect the reality of physical systems in terms of energy storage ability. It is first shown that this property may result from poorly chosen asymptotic behaviors. Another reason is that a fractional model can be viewed as a doubly infinite model. Indeed, its real state is of infinite dimension as it is distributed. Moreover, this state is distributed on an infinite domain. It is precisely this last feature that induces the ability to store an infinite energy, even if the fractional behavior is limited on a frequency band. As a consequence, even if fractional models permit to capture accurately the input-output dynamical behavior of many physical systems, these models do reflect the internal behavior of the modelled system which implies hard theoretical problems. Such problems may be avoided by the use of other models that exhibit the same input-output behavior but that do not have infinite energy storage ability. As a first attempt to solve this issue, a new class of models is thus introduced in the paper.

Keywords: Fractional systems, Linear systems, motion planning for distributed parameter systems
Abstract: The flatness property is studied for linear time-invariant fractional systems. In the framework of polynomial matrices of the fractional derivative operator, we give a characterization of fractionally flat outputs and a simple algorithm to compute them. These results are applied to a bidimensional thermal system approximated by a fractional transfer function of order 1/2. Temperature trajectory planning at a given point is then deduced without integration of the system equations.

Keywords: Fractional systems, Decoupling problems, Linear multivariable systems
Abstract: In recent years, more and more research work has demonstrated the advantages of using fractional order modeling and control techniques. However, many of these research focus on the single-input-single-output systems. In this paper, fractional order two-input-two-output processes are studied in terms of decoupling control. Three types of classic decoupling techniques for integer order processes are re-visited and are generalized to fractional order cases, which has not been addressed in the literature. This effort involves new problems other than those for integer order processes, such as the the properness, interaction analysis, and the frequency dependent relative gain array, etc. Simulation examples are given to illustrate these generalized decoupling methodologies and some notes on practical implementation are provided.

Keywords: Fractional systems, Continuous time system estimation, Identification for control, Estimation and filtering
Abstract: New sufficient linear-matrix-inequality conditions are provided to ensure the stability of a class of fractional-order systems by means of asymptotic observer-based feedbacks. It is shown that the search of the observer and the controller gains can be obtained by decoupling the necessary matrix inequalities that involve coupled gains. The obtained numerically tractable conditions are formulated as a set of strict linear matrix inequalities and compared to other sufficient conditions with equality constraints. Numerical computations are provided to show the straightforwardness and the efficiency of the proposed control designs.

Keywords: Fractional systems, Identification for control
Abstract: Necessary and sufficient conditions for the positivity and reachability of fractional descriptor positive discrete-time linear systems are established. The minimum energy control problem for the descriptor positive systems with bounded inputs is formulated and solved. Sufficient conditions for the existence of solution to the minimum energy control problem are given. Procedure for computation of optimal input sequences and minimal value of the performance index is proposed.

Keywords: Fractional systems, Regulation (linear case)
Abstract: This paper concerns fuel injection control of compressed natural gas engines. The main operating conditions are considered and for each one a fractional-order PI controller is designed. Then at each sudden change of rail pressure and injection timing, a change of the determined controller gains is scheduled. Switching between controllers is driven by step changes of the reference pressure. Robust stability of the designed closed-loop system is guaranteed by D-decomposition. Detailed simulation verifies both dynamic performance and robustness given by the controllers and stability of the switching.

Keywords: Identification and control methods, Cyber-Physical Systems
Abstract: Extending the success of model predictive control (MPC) technologies in embedded applications heavily depends on the capability of improving quadratic programming (QP) solvers. Improvements can be done in two directions: better algorithms that reduce the number of arithmetic operations required to compute a solution, and more efficient architectures in terms of speed, power consumption, memory occupancy and cost. This paper proposes a fixed point implementation of a proximal Newton method to solve optimization problems arising in input-constrained MPC. The main advantages of the algorithm are its fast asymptotic convergence rate and its relatively low computational cost per iteration since it the solution of a small linear system is required. A detailed analysis on the effects of quantization errors is presented, showing the robustness of the algorithm with respect to finite-precision computations. A hardware implementation with specific optimizations to minimize computation times and memory footprint is also described, demonstrating the viability of low-cost, low-power controllers for high-bandwidth MPC applications. The algorithm is shown to be very effective for embedded MPC applications through a number of simulation experiments.

Keywords: Design methodologies, Hardware/software co-design, Identification and control methods
Abstract: We propose in this paper an inexact dual gradient algorithm based on augmented Lagrangian theory and inexact information for the values of dual function and its gradient. We study the computational complexity certification of the proposed method and we provide estimates on primal and dual suboptimality and also on primal infeasibility. We also discuss implementation aspects of the proposed algorithm on constrained model predictive control problems for embedded linear systems and provide numerical tests to certify the efficiency of the method.

Keywords: Design methodologies, Cyber-Physical Systems
Abstract: This paper proposes a simple, non-iterative way of calculating low-complexity controlled contractive sets, and shows how such set can be used for controller design for systems with state and input constraints. The result is a low complexity controller, suitable for implementation in embedded control systems with modest computational power and available memory.

Keywords: Hardware/software co-design, Identification and control methods, Cyber-Physical Systems
Abstract: We propose a design methodology for explicit Model Predictive Control (MPC) that guarantees hard constraint satisfaction in the presence of nite precision arithmetic errors. The implementation of complex digital control techniques, like MPC, is becoming increasingly adopted in embedded systems, where reduced precision computation techniques are embraced to achieve fast execution and low power consumption. However, in a low precision implementation, constraint satisfaction is not guaranteed if innite precision is assumed during the algorithm design. To enforce constraint satisfaction under numerical errors, we use forward error analysis to compute an error bound on the output of the embedded controller. We treat this error as a state disturbance and use this to inform the design of a constraint-tightening robust controller. Benchmarks with a classical control problem, namely an inverted pendulum, show how it is possible to guarantee, by design, constraint satisfaction for embedded systems featuring low precision, fixed-point computations.

Keywords: Identification and control methods, Cyber-Physical Systems, Intelligent controllers
Abstract: A large class of algorithms for nonlinear model predictive control (MPC) and moving horizon estimation (MHE) is based on sequential quadratic programming and thus requires the solution of a sparse structured quadratic program (QP) at each sampling time. We propose a novel algorithm based on a dual two-level approach involving a nonsmooth version of Newton’s method that aims at combining sparsity exploitation features of an interior point method with warm-starting capabilities of an active-set method. We address algorithmic details and present the open-source implementation qpDUNES. The effectiveness of the solver in combination with the ACADO Code Generation tool for nonlinear MPC is demonstrated based on set of benchmark problems, showing significant performance increases compared to the established condensing-based approach, particularly for problems with long prediction horizons.

Keywords: Bio-signals analysis and interpretation, Identification and validation, Developments in measurement, signal processing
Abstract: The paper deals with a method for real-time frequency analysis of the electromyogram. The method has been proposed for usage in control systems of advanced robotics for different applications. The method includes robust real-time frequency estimation with preliminary usage of bandwidth filters. The method is easy to program and requires no additional resources. The paper presents an example of experimental approbation of proposed method.

Keywords: Bio-signals analysis and interpretation, Biomedical system modeling, simulation and visualization, Model formulation, experiment design
Abstract: The primary goal of this work is to classify Human Cognitive State (HCS) in human-machine cooperative control systems. A total of 10 young and fit volunteers were used as the experimental subjects. A set of 9 process control task conditions were programmed on an automation-enhanced Cabin Air Management System (aCAMS) originally developed to simulate with high-fidelity the life support system in aerospace applications. The psychophysiological and performance data of the subjects were recorded while they performed process control operations in collaboration with computer-based automatic control systems. The fuzzy C-Means (FCM) algorithm was used to classify the momentary HCSs into three categories: “Good", "Average" and "Risky" with certain degree of membership. The classification results indicated that the FCM-based classifier can achieve accurate HCS classification if the influential features are properly selected. The method proposed has potential to be applied to design adaptive task (or functional) allocation strategy in adaptive/intelligent human-machine control systems.

Keywords: Bio-signals analysis and interpretation
Abstract: This paper presents a novel electro-encephalography (EEG) signal processing chain designed to classify two levels of mental fatigue that appears after having spent a long time on a tedious task. The decrease in vigilance associated with mental fatigue makes it a dangerous state for operators in charge of complex systems. The processing chain, inspired from active brain computer interface computing, is implemented as follows: the EEG signal is initially filtered in a given frequency band and 15 electrodes out of 32 are then selected using a method based on Riemannian geometry. Next, a spatial filtering step is carried out using 6 common spatial pattern (CSP) filters. Lastly, a binary classification is performed using Fisher’s linear discriminant analysis (FLDA). The features used are the log variance of the 6 CSP filtered signals. The results obtained on 20 healthy volunteers are excellent with 100% of accuracy when the beta band is used. These performances drop to 84% and 68% when the same data are processed with a traditional signal processing chain where fatigue is classified by means of a FLDA classifier fed by the averaged power, or relative power, in the beta band extracted from 15 selected electrodes.

Keywords: Rehabilitation engineering and healthcare delivery, Bio-signals analysis and interpretation, Biomedical system modeling, simulation and visualization
Abstract: Multi-channel surface electromyography (sEMG) recognition has been investigated extensively by researchers over the past several decades. However, due to the nature of sEMG sensors, the more sensors are used, the greater chance for the sEMG to be influenced by environment noise. Furthermore, it is not feasible to use multi-sensors in some cases because of the bulky size of the sensors and the limited area of muscles. This paper proposes a novel sEMG recognition method based on the decomposition of single-channel sEMG. At first, sEMG is acquired while the participant does 5 predetermined hand gestures. Then, this signal is decomposed into its component motor unit potential trains (MUAPTs), which includes 4 steps: 2-order differential filtering, spikes detection, dimension reduction and clustering with Gaussian Mixture Model (GMM). Finally, 5 MUAPTs are obtained and used for hand gestures classification: four features, integral of absolute value (IAV), maximum value (MAX), median value of non-zero value (NonZeroMed) and index of NonZeroMed (Ind) are extracted to form feature matrix, which is then classified with the algorithm of Linear Discriminate Analysis (LDA). The classification results indicate this method can achieve an accuracy of 74.7% while the accuracy of traditional classification method for single-channel sEMG is about 52.6%

Keywords: Bio-signals analysis and interpretation, Developments in measurement, signal processing, Decision support and control
Abstract: Electronic auscultation is an efficient technique for capturing lung sounds and analysis of these sounds permits to evaluate the health condition of the respiratory system. Since the lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. This paper presents a method of analysis of lung sound signals using the Wavelet Packet Transform (WPT), and classification using artificial neural network (ANN). Lung sound signals were decomposed into the frequency sub-bands using the WPT and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. An ANN is trained and then applied to classify the lung sounds into one of three categories: normal, wheeze or crackle. This classifier was embedded in a microcontroller to provide an automated and portable device that will provide support for research and diagnosis in the evaluation of the respiratory function.

Keywords: Bio-signals analysis and interpretation, Modeling and identification, Healthcare management, disease control, critical care
Abstract: Quantitative analysis of pain is extremely difficult, because it is a subjective and individual sensation caused by the potential or existing observable tissue injury. For individuals with major cognitive or communicative impairments, it is necessary to carry out an effective method to quantify pain without self-report. Focusing on using physiological signals of the human body to evaluate pain, this paper proposes a new quantitative evaluation method with multiple physiological information to recognize the intensity of pain, which is induced by electrical stimulation that can be measured. When different intensity electrical stimulation applying on the subject, three-channel biosensors (BVP, ECG, SC) were used to obtain blood volume pulse, electrocardiogram and skin conductance. Experiments show that the proposed method can estimate the intensity of stimulation, which means that it can give the objective and quantitative evaluation of the pain

Keywords: Engine modelling and control
Abstract: Mean value engine models (MVEM) are well established for the study of IC engine air-path performance. Closed-loop control of the air-path is used to ensure the tracking of reference signals subject to constraints. To meet these requirements model predictive control (MPC) has been applied successfully to several air-path applications. This paper reports on the development of an MVEM and suitable MPC controller for transient performance evaluation which is then applied to the engine. A linearised prediction model is evaluated at each control time step, based on the current operating conditions, which ensures the model remains valid across a wide range of speeds and loads. This linearisation of the nonlinear model allows for the formulation of quadratic programming (QP) problems, which are efficiently solved as part of the proposed MPC algorithm. Special treatment in the measurement and calculation of the states and their derivatives is demonstrated to provide excellent tracking performance and a good match between the model and real-time experimental results. The systematic process of modelling coupled with MPC is shown to closely match test-bed performance, which verifies the methodology for studying air-path hardware alternatives.

Keywords: Engine modelling and control, Nonlinear and optimal automotive control
Abstract: Combining turbocharging with downsizing has now become a key technique for automotive engine to improve its performance as fuel economy and drivability. The technology potential is fully exploited only with an efficient air path management system. In this context, the present work addresses a novel control design based on feedback linearization for the turbocharged air system of a SI engine. Two control strategies have been investigated for this complex system: drivability optimization and fuel reduction. The effectiveness of the proposed approach is evaluated via an advanced engine simulator.

Keywords: Nonlinear and optimal automotive control, Engine modelling and control, Control architectures in automotive control
Abstract: The continuously increasing demands in terms of performance, environmental compatibility and safety motivate the growing interest for optimal control in automotive systems. In practice, however, these methods are seldom used, one of the reasons being the nonlinear nature of the plant which makes the computation more difficult. Several nonlinear optimal control methods have been tested for automotive systems, either starting from the optimization problem in terms of a Hamilton Jacobi Bellman equation or as a nonlinear extension of well established model predictive approaches. In this paper, we apply a method which in some sense combines both worlds, the C/GMRES method. In our implementation, no physical model is used but a nonlinear NARX model is derived from data. The resulting control law is applied to a production Diesel engine and tested against the production controller, showing the potential performance of the suggested method but also the design simplicity.

Keywords: Engine modelling and control
Abstract: This work presents an alternative way of exhaust gas recirculation (EGR) systems implementation in the turbocharged compression ignition engines. This sort of EGR has been studied as well as the most commonly used EGR system, where the intake and the exhaust manifolds are directly connected with a hose. However, in authors opinion the setup described in this paper has not been investigated enough, as most of the research papers concentrate on a conventional conguration modeling and control design. A problem of overcoming a positive scavenging pressure drop and delivering high amount of EGR is addressed in this article. It comes from the fact that in this kind of engines the exhaust pressure is lower than the intake, making it dicult to deliver high and stable portion of EGR over the engine operating range. A generic mean value engine model is developed in this work and an initial simple control structure is proposed.

Keywords: Engine modelling and control, Modeling, supervision, control and diagnosis of automotive systems
Abstract: In this paper, a model-based strategy for the air-path, fuel injection timing, and fuel pressure control of a heavy-duty Diesel engine is presented. The engine system is a six-cylinder Diesel engine, equipped with a dual-stage fixed geometry turbo system and a variable valve actuation (VVA) system. The VVA operates on the intake valves, and realizes a late Miller combustion cycle. The control strategy implemented is a multilinear - model predictive control (MPC), which manipulates the intake valve hold and closure timing, the fuel injection timing, and the needle opening pressure. The MPC objectives are: (i) to keep NOx emissions under a reference level, (ii) to keep the air-fuel ratio over a certain reference, while (iii) minimizing fuel consumption under other constraints. The use of a model predictive control strategy is motivated by the fact that the system is a multi-input multi-output system, with several constraints applied to it. This paper presents the applied control strategy and simulation results illustrating the potential of the proposed control. The simulation results show that the control strategy is applicable, and that the fuel consumption is minimized, but also that further refinements are required.

Keywords: Nonlinear and optimal automotive control, Automotive system identification and modelling
Abstract: To deal with the demanding goals of the restricted Diesel emission legislation the focus in the application of exhaust after treatment systems to fulfill these strict requirements moves more and more to model based design, optimization and control. Against this background a nonlinear model predictive control (MPC) for an exhaust after treatment system consisting of a DOC (Diesel oxidation catalyst), a DPF (Diesel particulate filter) and a SCR (selective catalytic reduction) is proposed. Similar to standard ammonia control schemes also in this study the dosing amount is the only adjustable input variable.But in contrast to these usually simple feed forward dosing approaches in this proposal also information about the nonlinear temperature behavior and the storage effects of the SCR - included in a nonlinear SCR model - can be considered for the ammonia control. Based on these improved knowledge of the system a nonlinear model predictive controller, also applicable for real time tests on the engine, is applied for SCR control leading to promising results in simulation.

Keywords: Intelligent robotics, Guidance navigation and control, Mobile robots
Abstract: The multi-robot coordination task is investigated in its Representation Space (RS). First, the RS model of the robot system as well as its prescribed task is formulated by clarifying the internal and external constraints a®ecting task realization. All constraints are depicted as unreachable areas in RS model. Then, whether a task is feasible or not is transformed to check 1) if the ¯nal representation denoting task realization of the robot system is reachable; and 2) if there is a connecting trajectory illustrating the process that the start representation varies to the ¯nal representation, amidst those unreachable areas denoting system constraints. Performance of di®erent planning algorithms are also evaluated upon the representation space, from which the optimal planning algorithm could thus be recognized. The task of multi-robot formation navigation in a warehouse environment is exempli¯ed to illustrate the performance of the proposed scheme: the formation of the robot system is maintained in motion with collisions avoided. Moreover, by incorporating the task oriented motion planning framework, it is capable of transforming an infeasible task into a feasible one with least adjustments in the system's formation to adapt to inner and/or external constraints during task realization.

Keywords: Autonomous robotic systems, Intelligent robotics, Robotics technology
Abstract: Future humanoid robots, working beside humans in complex dynamic environments, would be required to perform a wide repertoire of task. To this end traditional methods for deriving a control policy won't succeed, learning approaches are fundamental. Learning from demonstrations (LfD) techniques have appear as a means for developing intuitive control methods and generalize a skill based on a set of demonstrations. However, learning appropriate skills for every conceivable scenario the robot may encounter would still be a daunting undertaking. In this work we propose a framework for the generation and adaptation of new robot skills from previously learned skill models. Using previously learned models of a robot skill, and knowledge of the current task, the models of a skill is adapted to generate a new task by a merger or combination operation over the given robot skill models.

Keywords: Intelligent robotics, Robots manipulators, Autonomous robotic systems
Abstract: The paper presents a learning from demonstration approach to the catching of moving objects with a robot manipulator. The work explicitly reduces the impulse exchange by minimizing the relative velocity during the contact phase by learning relative relation between the object and the catcher instead of learning separate forward model of the object and trajectory generator for the robot. This contributes to the damage prevention on both, the object and the robot. The demonstrated catching movements are modelled by a Gaussian mixture model (GMM), which describes the probability distribution over the demonstrated data set. Gaussian mixture regression (GMR) is employed for motion prediction. A timing controller is designed to trade-off the catch location and the catching time. The learning scheme comprises the relative position and velocity profile between the object and the catcher. The approach allows to generate the movement of the catcher with guaranteed position and velocity convergence to the reference inferred from the position and velocity of the object. Experimental results with a five degree of freedom arm and a Photonic-Mixing-Device (PMD) camera for object detection validate the approach.

Keywords: Intelligent robotics, Autonomous robotic systems, Knowledge modelling and knowledge based systems
Abstract: The ultimate goal for humanoid robotics research is to develop humanoid robotic systems capable and flexible enough to handle the challenge of working alongside human in complex natural environments performing everyday tasks. To reach this goal it is key to develop appropriate structures in which to organize the acquire knowledge in a manner that allows the system to retrieve it in order to use it to fulfill its missions. In this work a knowledge base representation of the robot skills knowledge organized in terms of the relationships between objects, actions and event frames is proposed.

Keywords: Mobile robots, Autonomous robotic systems, Intelligent robotics
Abstract: This paper presents an application of a model checking framework to robotic search, particularly search problems known as pursuit-evasion that assume a smart, fast and evading target. Within the framework we can model different pursuit-evasion problems and thereby enable a direct and rigorous comparison between different problem formulations and their respective properties and algorithms. In addition, we enable the computation of new kinds of solutions to pursuit-evasion problems, so called strategies, that can consider multiple criteria, e.g. order of vertices or connectedness. These strategies are computed by satisfying a temporal logic formula by model checking. We present theorems that show the connection between a strategy and a temporal logic formula. We demonstrate our approach by applying it to two different graph-based pursuit-evasion problems and show how to enable a comparison. This work presents the first step and basis for further investigations of more rigorous and unified approaches to understand, compare and design pursuit-evasion models.

Keywords: Intelligent robotics, Human operator support, Knowledge modelling and knowledge based systems
Abstract: Task-level industrial robot programming is a mundane, error-prone activity requiring expertise and skill. Since humans easily communicate with natural language, it may be attractive to use speech or text as instruction means for robots. However, there has to be a substantial amount of knowledge in the system to translate the high-level language instructions to executable robot programs.

In this paper, the method of Stenmark and Nugues (2013) for natural language programming of robotized assembly tasks is extended. The core idea of the method is to use a generic semantic parser to produce a set of predicate-argument structures from the input sentences. The algorithm presented here facilitates extraction of more complicated, advanced task instructions involving cardinalities, conditionals, parallelism and constraint-bounded programs, besides plain sequences of commands.

The bottleneck of this approach is the availability of easily parametrizable robotic skills and functionalities in the system, rather than the natural language understanding by itself.

Keywords: Model predictive and optimization-based control
Abstract: Model predictive control has become a widely accepted strategy in industrial applications in the recent years. Often mentioned reasons for the success are the optimization based on a system model, consideration of constraints and an intuitive tuning process. However, as soon as unknown disturbances or model plant mismatch have to be taken into account the tuning effort to achieve offset-free tracking increases. In this work a novel approach for offset-free MPC is presented, which divides the tuning in two steps, the setup of a nominal MPC loop and an external reference adaptation. The inner nominal loop addresses the performance targets in the nominal case, decouples the system and essentially leads to a first order response. The second outer loop enables offset-free tracking in case of unknown disturbances and consists of feedback controllers adapting the reference. Due to the mentioned properties these controllers can be tuned separate and by known guidelines. To address conditions with active input constraints, additionally a conditional reference adaptation scheme is introduced. The tuning strategy is evaluated on a simulated linear Wood-Berry binary distillation column example.

Keywords: Model predictive and optimization-based control, Control of multi-scale systems, Advanced process control
Abstract: Motivated by applications in minerals processing, a novel model predictive control scheme is presented with non-constant prediction step size. In the proposed scheme, the sampling rate of the prediction and control horizons changes, with nearer steps having a shorter sampling period than ones in the more distant future. This approach allows for fine tuning of the control trajectory over the near horizon, whilst still allowing for long prediction and control horizons to account for slow dynamics. This may find application in many chemical and minerals processing plants where process units have multiple time-scale dynamics. Extensions to decentralized control of process networks are presented. The approach is illustrated using an industrial case study.

Keywords: Model predictive and optimization-based control, Real time optimization and control, Advanced control technology
Abstract: In Model Predictive Control (MPC), an optimization problem has to be solved at each sampling time, and this has traditionally limited the use of MPC to systems with slow dynamic. In this paper, we propose an efficient solution strategy for the unconstrained sub-problems that give the search-direction in Interior-Point (IP) methods for MPC, and that usually are the computational bottle-neck. This strategy combines a Riccati-like solver with the use of high-performance computing techniques: in particular, in this paper we explore the performance boost given by the use of single precision computation, and techniques such as inexact search direction and mixed precision computation. Finally, we test our HPMPC toolbox, a family of high-performance solvers tailored for MPC and implemented using these techniques, that is shown to be several times faster than current state-of-the-art solvers for linear MPC.

Keywords: Nonlinear process control, Model predictive and optimization-based control, Real time optimization and control
Abstract: This work focuses on the nonlinear control of instabilities in the combustion of a diluted dimethyl ether (DME) / air mixture under low temperature conditions. A nonlinear model predictive control (NMPC) is utilized to stabilize the combustion in simulation with the aim to specify minimum actuation dynamic requirements and develop control strategies for an existing experimental chemical reactor.

Keywords: Model predictive and optimization-based control, Process control applications, Process modeling and identification
Abstract: Advanced controllers such as model predictive control are in use for a wide range of application in the process industry. The potential utilization of such advanced predictive controllers is far from exhausted. One barrier for more widespread implementation is the lack of simple methodologies for advanced control design development which may be used by non experts in control theory. This paper presents and illustrates the use of a simple methodology to design an offset-free MPC based on ARX models. Hence a mechanistic process model is not required. The forced circulation evaporator by Newell and Lee is used to illustrate the offset-free MPC based on ARX models for a nonlinear multivariate process.

Keywords: Batch and semi-batch process control, Advanced control technology, Model predictive and optimization-based control
Abstract: In this paper, we consider about the control strategy design for batch processes with sever non-repetitive disturbances. An index is proposed to measure the repetitive extent of batch processes. An adaptive two dimensional iterative learning model predictive control (ILMPC) method is designed based on this index. The control algorithm is switched between an one-dimensional Model Predictive Control (MPC) and a two time dimensional ILMPC according to this index. Simulation shows the superior effects of the proposed algorithm in handling abrupt changes of plant dynamics.

Keywords: Analytic design, Application of nonlinear analysis and design, Robust control applications
Abstract: This paper considers the phenomenon of synchronization as a Control problem. In particular, a (robust) controller for achieving synchronization in pairs of second order nonlinear systems is designed. The design is inspired by the classical experiment on synchronization of pendulum clocks, as described by Christiaan Huygens. In the proposed control scheme, the systems do not interact directly but through an exogenous system. Ultimately, it is demonstrated that Huygens' controller, can be used to perform in-phase and anti-phase synchronized/coordinated tasks, with the advantage that small control gains are required (low energy consumption). The stability properties of the closed-loop system are analyzed by using perturbation theory and the proposed controller is experimentally validated on a pair of Cartesian robots.

Keywords: Supervision and testing
Abstract: In this work, we present a Hardware In the Loop (HIL) platform for testing control strategies designed and implemented in a commercial electronic control unit for industrial refrigerators. The architecture structure consists of four elements: (i) a real-time processor, in which a detailed model of the refrigerator is implemented; (ii) the industrial control unit device under test; (iii) an Input/Output (I/O) hardware interface which allows data communication between the real-time processor (model) and the industrial device (control) and (iv) a power PC in order to display and save experimental results. Introducing the HIL methodology in the refrigerator industry could reduce electronic control unit testing costs and save time in the production stage because it is possible to verify the control strategy performances in realtime for several operating conditions and different refrigerator models avoiding expensive and time-consuming test bench procedures.

Keywords: Digital implementation, Robust control applications, Convex optimization
Abstract: FIR (finite impulse response) digital filter design is a fundamental problem in signal processing. In particular, FIR approximation of analog filters (or systems) is ubiquitous not only in signal processing but also in digital implementation of controllers. In this article, we propose a new design method of an FIR digital filter that optimally approximates a given analog filter in the sense of minimizing the H-infinity norm of the sampled-data error system. By using the lifting technique and the KYP (Kalman–Yakubovich–Popov) lemma, we reduce the H-infinity optimization to a convex optimization described by an LMI (linear matrix inequality). We also extend the method to multi-rate and multi-delay systems. A design example is shown to illustrate the effectiveness of the proposed method.

Keywords: Control in neuroscience, Optimal control theory, Real-time control
Abstract: Neural population models describe the macroscopic neural activity that can be clinically recorded by an electroencephalogram (EEG). Such models are relevant for the investigation of many pathological neurological phenomena including epilepsy and Parkinson’s disease because the models operate on the same scale as the recorded data. Although several models exist in the neuroscience literature, none have leveraged the systematic approach of optimal control theory to design stimuli to treat such neurological conditions. In addition, these models have not yet reached the control community, which has instead largely focused on single neuron models. Here we present the model and a formulation of the seizure abatement goal expressed as an optimal control problem. We show several results including a realistic, noise-driven simulation where the control is applied as needed in a moving window.

Keywords: Analytic design, Systems with time-delays, Digital implementation
Abstract: Guaranteed dominant pole placement problem has already been considered in the literature (Journal of Process Control 19(2009):349–352). For the systems that are higher-order or have dead-time, pole placement procedure with PID controllers via modified Nyquist plot and root-locus has been proposed. Based on this idea, the dominant pole placement problem with discrete-PID controllers in z-domain is studied since it is important to take advantage of discrete-time domain representation during the pole placement procedure for time-delay systems. It is shown that modified Nyquist plot method is still valid in discrete-time domain and it is possible to find relevant discrete-PID controller parameters. Controller zeros are also considered in the study, since in the closed-loop controller zeros can disrupt the dominance. Success of the method demonstrated on example transfer functions.

Keywords: Analytic design, Data-based control, Tracking
Abstract: A new approach for the automatic generation of a dynamic feedforward control law for nonlinear dynamic systems represented by discrete-time local model networks (LMN) is proposed. The generic model structure of LMNs offers the opportunity to apply such a general and automated approach for model inversion, even when the overall model complexity may be high. LMNs can represent nonlinear dynamic systems of almost arbitrary complexity. Their generic structure allows the generation of a feedback linearizing input transformation in a highly automated way. This paper proposes and discusses such an approach for the important class of LMNs with minimum-phase property. As a subclass of the class of minimum-phase LMNs, only those without numerator dynamics are considered in this manuscript. By using the input transformation, which results from feedback linearization, the feedforward control law is obtained. It can then be applied online for any reference trajectory without pre-planning. Thus, by representing a nonlinear dynamic system by the generic structure of an LMN and applying the proposed feedforward control law generation, a dynamic feedforward control for such a nonlinear system can be found automatically. Finally, the effectiveness of the method is shown on results for a Wiener model.

Keywords: Control of renewable energy resources
Abstract: With increasing the intermitting generation such as wind power (WP) and photovoltaic (PV) in the ‎European power system, the conventional power plants online will be replaced by WP and PV. Thus, the ‎sources of conventional reserve available to the system will be reduced and fewer conventional plants ‎will be available online to share the regulation burden. Reserve management by conventional plants will ‎be difficult, especially in isolated power systems, which have no interconnection and limited capability of ‎power regulation. The power system inertia will be reduced, greater rates of frequency change will be ‎observed during system contingencies and conventional prime movers will have less time to react in ‎order to keep the frequency stable. Therefore, this paper focuses on the effects of increasing intermitting ‎generation on the frequency control of the European power system using different scenarios of the ‎intermittent generation after occurrence of abrupt variations in generation and load. The model was ‎created using SIMULINK/MATLAB software.‎

Keywords: Intelligent control of power systems, Smart grids
Abstract: This paper presents a distributed consensus-based approach to solve the economic dispatch problem with power generator constraints and transmission losses. Buses and transmission lines in the power system are modeled as nodes and edges in a communication graph, respectively. Each node exchanges information with its neighbors and runs two consensus algorithms in parallel, without relying on a centralized decision maker. A consensus algorithm plus a correction term is run to reach consensus on a Lagrangian variable to satisfy the generation-demand equality constraint, while another consensus algorithm is used to estimate the power mismatch in the network. Thus, each generating unit computes its output power according to its cost function. Advantages and limitations of the proposed approach are discussed. Finally, the algorithm is validated by means of numerical simulations on several benchmarks.

Keywords: Intelligent control of power systems, Smart grids, Modeling and simulation of power systems
Abstract: Electric Vehicle (EV) technology has developed rapidly in recent years, with the result that increasing levels of EV penetration are expected on electrical grids in the near future. The increasing electricity demand due to EVs is expected to provide many challenges for grid companies, and it is expected that it will be necessary to reinforce the current electrical grid infrastructure to cater for increasing loads at distribution level. However, by harnessing the power of Vehicle to Grid (V2G) technologies, groups of EVs could be harnessed to provide ancillary services to the grid. Current unbalance occurs at distribution level when currents are unbalanced between each of the phases. In this paper a distributed consensus algorithm is used to coordinate EV charging in order to minimise current unbalance. Simulation results demonstrate that the proposed algorithm is effective in rebalancing phase currents.

Keywords: Control system design, Optimal operation and control of power systems
Abstract: The energy savings achieved by implementing energy efficiency (EE) lighting projects are not sustainable and vanish rapidly given that project population decays without proper maintenance. In this study, it is proposed that an optimal maintenance plan (OMP) can be designed to decide the number of failed lighting EE devices to be replaced. This problem is formulated in a control system framework. Based on the existing study of the project population decay modelling, the project population dynamics are taken as the plant of the control system. The number of replaced lighting EE devices is taken as the input of the control system. As different lighting technologies have different population decay dynamics and different rebate tariffs, the control inputs can be optimally decided based on the project budget plan. The optimal maintenance planning problem is then translated into an optimal control problem and solved by a model predictive control (MPC) approach. By solving the problem, the project sponsors receive extra energy savings over a 10-year project crediting period. In addition, the project developers also obtain extra benefits with a small amount of reinvestment for the lighting project maintenance. The design of an optimal maintenance plan for a lighting retrofit project is taken as a case study to illustrate the effectiveness of the control system approach.

Keywords: Control system design, Smart grids
Abstract: The utility company has many motivations for modifying energy consumption patterns of consumers such as revenue decoupling and demand response programs. We model the utility company--consumer interaction as a reverse Stackelberg game and present an iterative algorithm to design incentives for consumers while estimating their utility functions. Incentives are designed using the aggregated as well as the disaggregated (device level) consumption data. We simulate the iterative control (incentive design) and estimation (utility learning and disaggregation) process for examples.

Keywords: Power systems stability, Modeling and simulation of power systems, Smart grids
Abstract: The knowledge of transmission line parameters involving series impedance and shunt admittance is crucial to power system analysis and power system state estimation. Particularly in areas of power systems protection it is critical to have accurate values of these parameters to determine the line ampacity. The line parameters change according to the load level and weather conditions, so it is necessary to identify the line parameters in real time. The paper deals with the on-line parameter identification of transmission lines from synchronized measurements of current and voltage phasors provided by the phasor measurement units. The series and shunt line parameters are recursively estimated from the phasor measurements and weather conditions using the extended Kalman filter. Contrary to the other works, an actual line temperature and reference resistance are estimated, which makes possible to monitor the actual state of the transmission line in order to determine an actual reserve or prediction of future transmission apacity. A case study based on data measured from a 110kV overhead transmission line is presented to demonstrate the effectiveness of the proposed approach.

Keywords: Nonlinear cooperative control, UAVs, stability of delay systems
Abstract: This paper addresses the coordinated control problem of underactuated thrust-propelled vehicles in the presence of irregular communication delays under a directed communication graph topology. We propose a distributed control algorithm that drives the vehicles to a prescribed formation provided that the communication graph contains a spanning tree. Using the multi-dimensional small-gain approach, we show that the proposed control scheme is robust with respect to varying communication delays, which can be discontinuous with unknown upper bounds. Simulations are provided to illustrate the effectiveness of the proposed control algorithm.

Keywords: UAVs, Control of constrained systems, Asymptotic stabilization
Abstract: This paper considers the question of landing an Unmanned Aerial Vehicles (UAV) using a single monocular camera as the primary exteroceptive sensing modality. The proposed control law is based on tracking a single point feature, representing the desired landing point on a ground plane, along with optical flow computed over the full image. The bearing of the desired landing point is used as a driving term to force convergence, while the optical flow is used to provide a damping force that guarantees both obstacle avoidance as well as damping the convergence of the vehicle to the ground plane ensuring a soft touchdown. A detailed analysis of the system closed-loop dynamics is undertaken and the response of the system is verified in simulation.

Keywords: UAVs, Robust control, Disturbance rejection
Abstract: This work proposes a feedback control strategy to let the dynamics of an underactuated Vertical Take-Off and Landing (VTOL) aerial vehicle to track a desired trajectory thanks to an adaptive robust compensation of the aerodynamic disturbances. The novelty of the proposed approach consists in employing an aerodynamic disturbance observer derived using the NonLinear Geometric Approach and Radial Basis Functions (RBF). The obtained estimation is directly employed by a nonlinear robust feedback law which relies on a cascade control paradigm in which the attitude dynamics and the position dynamics of the vehicle play the role of the inner and of the outer loop, respectively. The robustness of the proposed approach is also demonstrated by means of simulation results in which the aerodynamic model of a multi-propeller aircraft is considered.

Keywords: UAVs, Stability of nonlinear systems, Constrained control
Abstract: This paper focuses on the design of a stabilizing control law for an aerial vehicle which is physically connected to a ground station by means of a tether cable. When the cable is taut, the resulting dynamic model is shown to be characterized by a new set of equilibria which untethered aircraft are unable to maintain in steady state.

The control objective is to steer the UAV to a desired set-point while maintaining the cable taut at all times. This leads to a nonlinear control problem subject to constraints. A cascade control scheme is proposed and proven to asymptotically stabilize the overall system by means of ISS arguments. Constraint satisfaction is guaranteed using a modified thrust vector control coupled with a reference governor strategy. The effectiveness of the proposed control strategy is shown via numerical simulations.

Keywords: Passivity-based control, UAVs, Lagrangian and Hamiltonian systems
Abstract: This work presents an IDA-PBC control methodology for a quadrotor helicopter to perform path tracking. The control law is designed considering the trajectory tracking formulation for underactuated systems. The goal is to regulate the controlled degrees of freedom (DOF), the translational position, x, y and z, and the yaw angle, psi, and stabilize the remaining DOF, the roll and pitch angles, phi and theta, respectively, using only one control loop. This formulation leads to a set of partial differential equations constraints due to the underactuation degree of the system. The IDA-PBC controller performance is corroborated through simulation results, being compared with a backstepping controller.

Keywords: Robust control, UAVs, Lyapunov methods
Abstract: This work presents, the design and the corresponding analysis of a nonlinear controller for an aircraft system subject to uncertainties in the dynamics and additive state-dependent nonlinear disturbance-like terms. Specically; dynamic inversion technique in conjunction with a robust integral of the signum of the error feedback and an adaptive term is utilized in the overall controller design. Lyapunov based stability analysis techniques are then utilized to prove global asymptotic convergence of the tracking error.

Keywords: Nonlinear system identification, Time series modelling, Bayesian methods
Abstract: In this paper, we present a distributed algorithm for the reconstruction of large-scale nonlinear networks. In particular, we focus on the identification from time-series data of the nonlinear functional forms and associated parameters of large-scale nonlinear networks. In the previous work, a nonlinear network reconstruction problem was formulated as a nonconvex optimisation problem based on the combination of a marginal likelihood maximisation procedure with sparsity inducing priors. In this paper, we derive an iterative reweighted lasso algorithm to solve the initial nonconvex optimisation problem based on the concave-convex procedure (CCCP). Moreover, by exploiting the structure of the objective function of this algorithm, a distributed algorithm is designed. To this end, we apply the alternating direction method of multipliers (ADMM) to decompose the original problem into several subproblems. To illustrate the effectiveness of the proposed methods, we use our approach to identify a network of interconnected Kuramoto oscillators with different network sizes (500-100,000 nodes).

Keywords: Nonlinear system identification, Frequency domain identification, Continuous time system estimation
Abstract: The problem Wiener systems identification is addressed in presence of hysteresis nonlinearities, presently described by the Bouc-Wen model. The latter is nonlinear differential equation involving unknown parameters, some of which coming in nonlinearly. Except for stability, the linear subsystem is arbitrary and, in particular, it is not given a particular structure. By using sine excitations, the identification problem is reformulated as a set of nonlinear prediction-error optimization problems. The latter will be coped with using nonlinear least squares estimators which will formally be shown to be consistent.

Keywords: Nonlinear system identification, Quantized systems, Input and excitation design
Abstract: This paper considers the linear system identification with batched binary-valued observations. An iterative parameter estimate algorithm is constructed to achieve the Maximum Likelihood (ML) estimate. The first interesting result is that there exists at most one finite ML solution for this specific maximum likelihood problem, which is induced by the fact that the Hessian matrix of the log-likelihood function is negative definite under binary data and Gaussian system noises. The global concave property and local strongly concave property of the log-likelihood function are obtained. Under mild conditions on the system input, the ML function can be proved to have unique maximum point. The second main result is that the proposed iterative estimate algorithm converges to a fixed vector with an exponential rate which are proved by constructing a Lyapunov function. The more interesting result is that the limit of the iterative algorithm achieves the maximization of the ML function. Numerical simulations are illustrated to support the theoretical results obtained in this paper well.

Keywords: Nonlinear system identification, Bounded error identification, Stochastic system identification
Abstract: In modeling of a stochastic nonlinear dynamic system from input-output data, it may be of interest to model uncertainty in the underlying system besides predicting a most likely or average response of the system. Due to stochasticity in the system behavior, the data obtained for identification can be considered as one realization of the underlying stochastic phenomenon. In order to effectively deal with the identification of such systems, it may be advantageous to repeat the identification experiment multiple times under similar conditions. The multiple input-output time series generated in this way thus contain information about stochastic variations within the system. This paper presents one of the possible approaches to effectively deal with identification in such scenario in the framework of Nonlinear Output Error (NOE) Takagi-Sugeno (TS) fuzzy models. Based on extended Chebyshev's inequality for finite samples, the lower and upper boundaries of the output time-series are obtained using (1-alpha) confidence interval (envelops of the response). The proposed identification algorithm provides a model for predicting the most likely value as well as the boundary models for predicting the envelops of the output signal. The experimental results for an electro-mechanical throttle shows the applicability and validity of the proposed approach.

Keywords: Nonlinear system identification, Statistical data analysis, Frequency domain identification
Abstract: Detecting nonlinearity in experimental data is a key challenge within system identication. Based on two new models, some aspects of nonlinearity relevant for system identication are discussed. The models are used to illustrate dierences between three dierent methods for data-based testing for nonlinearity in dynamical systems. The use of Fourier-based surrogate data is found to be the most reliable approach for detecting nonlinearity in data from dynamical systems.

Keywords: Nonlinear system identification
Abstract: Posynomials are nonnegative combinations of monomials with possibly fractional and both positive and negative exponents. Posynomial models are widely used in various engineering design endeavors, such as circuits, aerospace and structural design, mainly due to the fact that design problems cast in terms of posynomial objectives and constraints can be solved efficiently by means of a convex optimization technique known as geometric programming (GP). However, while quite a vast literature exists on GP-based design, very few contributions can yet be found on the problem of identifying posynomial models from experimental data. Posynomial identification amounts to determining not only the coefficients of the combination, but also the exponents in the monomials, which renders the identification problem numerically hard. In this paper, we outline an approach to the identification of both multivariate polynomial and posynomial models, based on the expansion on a given large-scale basis of monomials. The model is then identified by seeking coefficients of the combination that minimize a mixed objective, composed by a term representing the fitting error and a term inducing sparsity in the representation, which result in a problem formulation of the ``square-root LASSO'' type, with nonnegativity constraints on the variables. We propose to solve the problem via a sequential coordinate-descent scheme, which is suitable for large-scale implementations.

Keywords: Estimation and filtering, Particle filtering/Monte Carlo methods, Simulation of stochastic systems
Abstract: Feedback control system performance can be decreasing during its operation because of the components' wear or degradation. To deal with such loss of efficiency, a research aspect of dependability concerns fault-tolerant control (FTC) strategies which give the feedback control system the ability to overcome faults. In this way, the RUL (remaining useful life) becomes valuable information which can be integrated in controller design in efforts to find a satisfactory trade-off between control system performances and components' lifetime. The main aim of this paper is twofold. On one hand, it proposes an integrated model which jointly describes the state of the feedback control system and the actuators degradation. On the other hand, a probabilistic model-based framework is presented in order to assess the RUL of the deteriorating feedback system. No special monitoring device is used to observe the health status of actuator, thus the measurements of closed system response are considered as the only available health information. The RUL is computed by a two-step technique. First, the system state regarding the available observations is estimated on-line by using the Particle Filter method. Then, the reliability of the system is computed with a classical Monte Carlo method. In order to illustrate this approach, a well-known simulated double-tank level control system is used.

Keywords: Estimation and filtering, Filtering and smoothing
Abstract: A time domain signal tracking algorithm is proposed to estimate the frequency of a chirp type global navigation satellite system (GNSS) interference. The unknown interference frequency of GNSS is estimated by the received signal using properties of trigonometrical functions, but this value has a lot of error caused by measurement noise and the frequency change of the interference. In order to reduce the error, an adaptive fading Kalman filter is applied to the proposed algorithm. Furthermore, a low pass differentiator (LPD) and a pattern enhancement algorithm are used to estimate the sweep period of chirp type interference. The performance of the tracking algorithm is verified by comparing to conventional algorithms. By analyzing the results of comparisons, the proposed algorithm has better tracking performance than conventional algorithms when the jammer to signal ratio (J/S) of GNSS interference signal is more than 30dB, which seriously affects the accuracy of the GNSS.

Keywords: Estimation and filtering
Abstract: In this paper we consider the filter design problem for very high dimensional systems. Assuming the hypothesis on separability of the vertical and horizontal structure for the error covariance, the number of unknown elements in the error covariance is reduced drastically and are estimated from generated error samples. A low-cost filtering algorithm is thus determined and parameterized up to some pertinent gain coefficients to be tuned to optimize the filter performance. Results from the experiment on assimilation of the sea surface height (SSH) into an oceanic numerical model demonstrate the high effectiveness of the proposed filtering approach.

Keywords: Estimation and filtering, Distributed control and estimation, Filtering and smoothing
Abstract: Distributed implementations of state estimation algorithms generally have in common that each node in a networked system computes an estimate on the entire global state. Accordingly, each node has to store and compute an estimate of the same state vector irrespective of whether its sensors can only observe a small part of it. In particular, the task of monitoring large-scale phenomena renders such distributed estimation approaches impractical due to the sheer size of the corresponding state vector. In order to reduce the workload of the nodes, the state vector to be estimated is subdivided into smaller, possibly overlapping parts. In this situation, fusion does not only refer to the computation of an improved estimate but also to the task of reassembling an estimate for the entire state from the locally computed estimates of unequal state vectors. However, existing fusion methods require equal state representations and, hence, cannot be employed. For that reason, a fusion strategy for estimates of unequal and possibly overlapping state vectors is derived that minimizes the mean squared estimation error. For the situation of unknown cross-correlations between local estimation errors, also a conservative fusion strategy is proposed.

Keywords: Estimation and filtering
Abstract: The central problem in Adaptive Optics feedback control is the reconstruction of the aberrated wavefront from wavefront sensor measurements. We recently presented a novel algorithm to compute the wavefront estimate directly from (Shack-)Hartmann intensity images instead of using the classical centroid algorithm to approximate the local wavefront slopes. The novel algorithm allows a distributed linearization of the model describing the imaging process through the use of a B-spline parametrization of the wavefront. This linearization enables the estimation of the wavefront via a linear least squares solver. A major bottleneck of this new algorithm is the computational complexity that stems from the large number of pixels with each pixel giving rise to one row of the overdetermined set of equations. In this paper, a compression method is proposed to speed up this new reconstruction method by only using a small percentage of the given intensities to make it applicable for real-time Adaptive Optics. Numerical simulations for open-loop and closed-loop show that reducing the data on the one hand dramatically reduces the number of measurements, but on the other hand does not cause any significant loss in accuracy or robustness of the reconstructed wavefront estimate.

Keywords: Estimation and filtering, Stochastic control and game theory, Nonlinear adaptive control
Abstract: We present discrete-time stochastic extremum seeking algorithms and prove their convergence using stochastic averaging theory that we recently developed. First, we provide a discrete stochastic extremum seeking algorithm for a static map, in which measurement noise is considered and an ergodic discrete-time stochastic process is used as the excitation signal. Second, for discrete-time nonlinear dynamical systems, in which the output equilibrium map has an extremum, we present a discrete-time stochastic extremum seeking scheme and, with a singular perturbation reduction, we prove the stability of the reduced system. Compared with classical stochastic approximation methods, while the convergence that we prove is in a weaker sense, the conditions of the algorithm are easy to verify and no requirements (e.g., boundedness) are imposed on the algorithm itself.

Keywords: Model-driven systems engineering, Architectures and software tools for enterprise integration and networking in manufacturing, Enterprise modelling and BPM
Abstract: The product variety management is a key process to deal with the flexibility requested by the mass customisation. In this paper we show that current variety-modelling methods miss a customer representation: without a proper assessment of the customers is not possible to define the product variety that has to be developed to meet the requirements of a customer segment. Here we present an innovative approach to rationalise the product variety, i.e. to link each product variant to the customer profile who needs it. The aim is to optimise the product variety avoiding excesses (variants not related to a customer), lacks (customers not related to a variant) or redundancies (two or more variants proposed to a customer). An overview of customer modelling approaches in the classic product design (non-customisable) is presented. The innovative approach is here developed using system-thinking concepts. A knowledge-based system that uses this approach is designed. Finally the approach is explained using a real industrial case of a quasi-real coil design process.

Keywords: Collaborative networked organizations principles, Enterprise modelling and BPM, Systems interoperability
Abstract: Collaboration between enterprises has moved from regional and national environments to a global one. This has tremendously increased the need for information exchange between collaborating partners. However, non-compatible (heterogeneous) ICT environments and inconsistent semantic and syntax of information to be exchanged are very often barriers in the interoperation process. Nevertheless, the relevant semantic information is usually collected in enterprise models. This information, amended by the corresponding syntax, can be collected in Object Capability Profiles that describe the required and provided information to be exchanged by the partners. An even more elaborate solution could be a new model view that collects all information needed to support intra- and inter-enterprise information exchanges. The authors first propose the exploration of object capability profiles for inter-organisational communication or interoperation applied to CIMOSA and the related international standard CEN/ISO 19440. Object Capability Profiles of objects potentially involved in information exchange will identify both semantic and syntax of the information to be exchanged. Comparison between required and provided information will detect any mismatch between the two and would allow automatic or manual corrections. In addition, collaboration aspects are proposed to be modelled in a specific modelling view called Collaboration View. After recalling elements of the CIMOSA modelling language, the paper presents a proposal for both the Object Capability Profiling and the Collaboration View. A simplified illustrative example demonstrates the applicability of the two proposals.

Keywords: Enterprise integration, Architectures and software tools for enterprise integration and networking in manufacturing, Enterprise Reference Models and Their Verification, Validation, and Accreditation
Abstract: Apart from the 20-year anniversary in 2014 of the first publication of the GERAM (Generalised Enterprise Reference Architecture and Methodology) Enterprise Architecture Framework, the timeliness of this paper lies in the new interest in the use of systems theory in Enterprise Architecture (EA), and consequently, 'light-weight' architecture frameworks (AFs). Thus, this paper is about the use of systems thinking and systems theory in EA and about how it is possible to reconcile and understand, based on a single overarching framework, the interplay of two major enterprise change endeavours: on the one hand enterprise engineering (i.e. deliberate change) and on the other hand evolutionary, organic change. The paper also attempts to show how such change processes can be illustrated by employing systems thinking to construct dynamic business models: the evolution of these concepts is exemplified with some past applications in networked enterprise building and more recent proposals in environmental, disaster and healthcare management. Finally, the paper attempts to plot the way GERAM will continue to contribute to society in the context of future challenges and emerging opportunities.

Keywords: Enterprise modelling and BPM, Digital enterprise, Model-driven systems engineering
Abstract: Business and information system alignment is key to improve business performance. Yet, little studies show practically how to achieve an efficient fit between business and IT. This paper proposes a methodology for accompanying the evolution of factory information and control systems for complex enterprises in an agile environment, from a functional standpoint. The methodology relies on capturing user requirements in a Reference Model and finding acceptable compromises of IT development, to reach the alignment incrementally. The methodology has been designed at 300mm unit of STMicroelectronics in Crolles. It is currently under deployment.

Keywords: Enterprise modelling and BPM, Business process management systems, Enterprise model validation
Abstract: With increased market competition and advances in modern manufacturing technologies, requirements on customer orders and manufacturing conditions have become more diversified. This paper addresses a dynamic production planning problem for a steel plate fabrication plant with practical flexibility of both customers and manufacturing, such as steel slab-plate matching rules, plate substitution options, production line assignment. These flexibility factors can provide the decision maker with auxiliary options to satisfy customer requirements through realizable production planning. The steel slab-plate matching rules combined with plate substitution options are visualized by a networked graph and formulated by a set based design. A mixed-integer nonlinear programming (MINLP) model that incorporates various manufacturing constraints and the flexibility is proposed to simultaneously optimize production strategy and provide practical managerial information such as backlogging/inventory level, capacity availability, and also steel slab demand.. Linearization methods are used to transform the original MINLP problem into mixed integer linear programming (MILP) model resulting in easier and quicker solving methods. A real industrial steel plate fabrication plant is used as a case and illustrates the effectiveness and applicability of the proposed method.

Keywords: Hybrid and switched systems modeling, Switching control, Control over networks
Abstract: Video streaming is becoming the application generating the largest fraction of the Internet traffic. Adaptive video streaming adds to classic video streaming the feature of dynamically adapting the video bitrate to track the time-varying network available bandwidth, avoid playback interruptions and ensure the delivery of the best video quality. In this paper we focus on the adaptive video streaming control system employed by Akamai, a major CDN operator whose video delivery system is used by several video streaming platforms, including Livestream. Differently from the typical client-side control, Akamai employs an interesting and unique hybrid client/server control architecture. Our purpose is to derive and validate a closed loop mathematical model of the control system, which turns out to be a hybrid automaton. The model is then analyzed to derive key properties which can be used to properly tune the controller parameters.

Keywords: Discrete event modeling and simulation, Identification for control
Abstract: This paper considers a dynamic model for a direct-contact membrane distillation process based on a 2D advection-diffusion equation. Thorough analysis has been carried on the equation including descritization using an unconditionally stable algorithm with the aid of Alternating Direction Implicit method (ADI). Simulations have showed a consistency between the proposed model results and the expected behavior from the experiments. Temperature prole distribution along each membrane side, in addition to flux and flow rate variations are depicted. Distribution of temperature of all the points in feed and permeate containers has been obtained with their evolution with time. The proposed model has been validated with a data set obtained from experimental works. The comparison between the proposed model and experiments showed a matching with an error percentage less than 5%. An optimization technique was employed to find optimum values for some key parameters in the process to get certain amount of mass flux above desired values.

Keywords: Discrete event modeling and simulation, Reachability analysis, verification and abstraction of hybrid systems, Diagnosis of discrete event and hybrid systems
Abstract: One of the main obstacle that prevents model checking from being widely used in industrial control systems is the complexity of building formal models out of PLC programs, especially when timing aspects need to be integrated. This paper brings an answer to this obstacle by proposing a methodology to model and verify timing aspects of PLC programs. Two approaches are proposed to allow the users to balance the trade-off between the complexity of the model, i.e. its number of states, and the set of specifications possible to be verified. A tool supporting the methodology which allows to produce models for different model checkers directly from PLC programs has been developed. Verification of timing aspects for real-life PLC programs are presented in this paper using NuSMV.

Keywords: Hybrid and switched systems modeling, Stability and stabilization of hybrid systems
Abstract: The impact of time delay on the dynamics of a hybrid model of pulsatile feedback endocrine regulation is investigated. The model in hand can be seen as an impulsive and delayed version of the popular in computational biology Goodwin oscillator, where the feedback is implemented by means of pulse modulation. The value of the time delay is related to the duration of the time interval between the firing times of the feedback impulses. Under the assumption of a cascade structure of the continuous part of the model, the hybrid dynamics of the closed-loop system are shown to be governed by a discrete mapping propagating through the firing times of the impulsive feedback. Conditions for existence and stability of periodic solutions of the model are obtained. Bifurcation analysis of the mapping reveals the phenomenon of bistability arising for larger time delay values but not observed for the smaller ones.

Keywords: Diagnosis of discrete event and hybrid systems, Fault detection and diagnosis
Abstract: In this paper, a time varying disturbance observer subject to sampled data's measurements is proposed for a class of non-linear systems. The proposed observer combines the advantage of a high gain structure in terms of convergence speed and an output predictor which remains continuous between the sampling times. The exponential convergence of the proposed observer is proved using a Lyapunov function adapted to impulsive systems.

Keywords: Hybrid and switched systems modeling, Switching control
Abstract: The generalized switched server (GSS) system model has been proposed to address the task balancing control problems of the multi-units systems with control constraints. Similar to the stability of a control system, the balanceability of a GSS system investigates whether the system could be driven to a task balancing status or not. The heterogeneous GSS systems, in which the dynamical models of the multiple parallel units are not the same, are commonly confronted in practice. This paper mainly concerns with the system balanceabililty analysis, especially, the FRR switching policy for heterogeneous systems. The GSS system model and the related switching control policy are first introduced, then the balanceability problems are elaborated, and finally a sufficient condition for the balanceability of the FRR policy for the heterogeneous systems is presented.

Keywords: Coordinated control, Cooperative control, Autonomous underwater vehicles
Abstract: The paper introduces the control and coordination problems encountered within the employment of autonomous underwater floating manipulators for object retrieval from the sea floor. To this respect, the employment of unifying control framework, capable of guaranteeing the necessary system agility and flexibility, is outlined. Some experimental results from the TRIDENT FP7 project are presented, along with the possible extension of the proposed framework to the case of dual arm manipulators.

Keywords: Sensors and actuators, Mechanical design of autonomous vehicles, Unmanned marine vehicles
Abstract: In this paper, a novel intrinsic tactile sensor for underwater robotic applications is presented, along with some experimental results. The sensor is based on optoelectronic components and therefore its design is quite simple and reliable. Moreover, it is suitable to be adapted to different mechanical configurations, allowing its integration in e.g. the fingers of robotic hands or on the wrist of a robot arm. In this paper, the basic principle and the design of the sensor are presented, describing also some prototypes developed for underwater applications. Experimental data are presented and discussed to illustrate the main features of the sensors.

Keywords: Autonomous underwater vehicles, Acoustic-Based Networked Control and Navigation, Localization
Abstract: The CommsNet 2013 experiment took place in September 2013 in the La Spezia Gulf, North Tyrrhenian Sea. Organized and scientifically led by the NATO S&T Org. Ctr. for Maritime Research and Experimentation (CMRE, formerly NURC), with the participation of several research institutions, the experiment included among its objectives the evaluation of on-board acoustic Ultra-Short Base Line (USBL) systems for navigation and localization of Autonomous Underwater Vehicles (AUVs). The ISME groups of the Universities of Florence and Pisa jointly participated to the experiment with one Typhoon class vehicle. This is a 300 m depth rated AUV with acoustic communication capabilities originally developed by the two groups for archaeological search. The CommsNet 2013 Typhoon, equipped with an acoustic modem/USBL head, navigated within the fixed nodes acoustic network deployed by CMRE. This allows the comparison between inertial navigation, acoustic self-localization and ground truth represented by GPS signals (when the vehicle was at the surface). The preliminary results of the experiment show that the acoustic USBL self-localization is effective, and it has the potential to improve the overall vehicle navigation capabilities.

Keywords: Acoustic-Based Networked Control and Navigation, Autonomous underwater vehicles, Localization
Abstract: The use of Long Baseline (LBL) systems is quite consolidated in the underwater domain, especially within applications where it is important to precisely localize submerged devices close to the sea bottom. Indeed with a LBL acoustic array the nominal positioning accuracy for seabed applications results to be not dependent on the depth and almost constant at any point inside the area delimited by the transponders. Despite the above advantages, the achievable accuracy of LBL systems is actually affected by different factors, mainly related with technical limits of the used instruments and with the level of knowledge of the physical characteristics of the acoustic medium. Another important element, possibly reducing the precision, concerns the way the LBL system is operated, and is related with the calibration of the acoustic array after its deployment on the sea bottom. Indeed if the positions of all the transponders are not perfectly known, errors in the localization procedure unavoidably arise. The paper specifically focuses on this last aspect and investigates the linkage existing between the error on the position of transponders and the resulting error in the localization procedure. A detailed theoretical analysis of the problem is proposed and for some basic transponders geometries a closed form relationship is obtained. Some simulations are finally reported to support the achieved results.

Keywords: Autonomous surface vehicles, Decision support systems in marine systems, Sensors and actuators
Abstract: Automatic Target Recognition (ATR) is a key element needed to make Mine Countermeasure missions using robots entirely autonomous. While there has been much progress in applying ATR algorithms on high-resolution Synthetic Aperture Sonar (SAS) and sidescan sonar data, performing ATR with a low cost Forward Looking Sonar (FLS) is much more challenging. An algorithm for the detection of underwater man-made objects in FLS previously developed can work in real-time although it suffers considerably from typical noise in sonar images and false alarms. The work presented here shows that ATR algorithms can be exercised on sonar mosaics built also in real-time instead of raw data coming from the FLS. The use of mosaics can help the detection of the targets by reducing some noise (including harmonics from other acoustic devices mounted on the robot) and giving a better contrast to the images to be processed. Moreover, mosaic images can be useful for post-processing and data analysis. The mosaicking algorithm also runs in real-time to maintain the performance of the system and to be useful in real missions. It was tested both on data previously collected and in real experiments with different set-ups and with different sonars. The wide range of results obtained with different surface vehicles and in different situations demonstrate the usefulness of the method.

Keywords: Autonomous underwater vehicles, Sensors and actuators, Sensor integration and perception
Abstract: This paper describes the development and performance assessment of a low-cost stereo vision system for underwater object detection. The system has been conceived as a prototype to investigate the performance, power consumption, and thermal dissipation tradeoffs involved in designing an embedded stereo vision unit for underwater operation. The embedded system has been experimentally assessed in underwater object detection tasks. The system has proven thermally stable and capable of guaranteeing a level of autonomy of at least two hours of video acquisition. Several algorithms for mono and stereo image processing have been evaluated to assess their effectiveness in the underwater environment along with their suitability in presence of constrained computational and energy resources. Evaluation of the stereo vision system in detecting simple objects has shown strong limitations of commodity off-the-shelf sensors when used in underwater perception. Nevertheless, the prototype described in this work provides insights for development of more advanced vision systems suitable for underwater vehicles.

Keywords: High accuracy pointing
Abstract: The paper describes the experimental ADCS mode of the NEMO-HD satellite, built in the co-operation between SPACE-SI and Space Flight Laboratory, University of Toronto. In the experimental ADCS mode several imaging modes will be implemented such as fixed target observation, path tracking target observation, area sweep mode and spread area sweep mode. A Matlab-Simulink based simulator was designed, which accurately incorporates the astrodynamics and the kinematics of the satellite in real time. It also provides the images of the observed target by means of the Active-X connection to the Google Earth. The results of simulations demonstrate the applicability of the proposed imaging modes to be implemented on the NEMO-HD satellite, which is planned to be launched in 2015.

Keywords: Guidance, navigation and control of vehicles
Abstract: In this paper, a novel terminal guidance law is proposed to solve the problem of exo-atmospheric interception. It is designed based on the proportional navigation (PN) and the classical optimal sliding-mode guidance (OSMG). It overcomes the shortcoming of these two traditional guidance laws and inherits their merits. Particularly, in the scenario of exo-atmospheric interception, when the maneuvering information of the targets are unavailable, the proposed guidance law has superior performance than the traditional guidance law. Briefly, to enhance the interceptive performance, we introduce the optimal control and sliding-mode control methods to the guidance law for maneuvering target's interception. On the other hand, the traditional proportional navigation law is introduced to intercept the target with a constant speed. After that, a fuzzy switching function is provided to harmonize both situations above, according to the real-time estimation of maneuver. To guarantee the stability of the law, a Lyapunov based condition is obtained. Finally, different from the two-dimensional simulation in most of the literatures of terminal guidance law researches, we illustrate our method in the nonlinear discrete three-dimensional simulation environment. Compared with the pure OSMG and PN, the proposed law performs better.

Keywords: Guidance, navigation and control of vehicles, Positioning Systems, Navigation, Guidance and Control
Abstract: This paper presents the hybrid database referenced navigation algorithm using terrain height and gravity anomaly information together. Geophysical information database can be used for navigation with inertial sensors on the GPS denied environment. Terrain height is most commonly used as secondary navigation information with INS. However, terrain referenced navigation is sometimes impossible to estimate the position because of the lack of uniqueness on the flat terrain. Therefore, we propose the hybrid database referenced navigation technique which utilizes a gravity anomaly along with terrain height information. In this paper, the point mass filter is used for estimation of the vehicle’s position. And weighting logic for hybrid database referenced navigation is designed based on the kurtosis of posterior pdf(probability density function) because that of posterior pdf has a proportional relationship to the estimation accuracy. Each estimation result is mixed together according to the kurtosis. As a result, it is shown by the numerical simulation that the proposed method has a better performance than the navigation result obtained from individual database.

Keywords: Guidance, navigation and control of vehicles, Control of systems in vehicles, Modeling and simulation of transportation systems
Abstract: This paper discusses the design and validation of an integrated long range flexible aircraft load controller, at a single flight/mass configuration. The contributions of the paper are in twofold: (i) first, a very recent frequency-limited model approximation technique is used to reduce the large-scale aeroservoelastic aircraft model over a finite frequency support while guaranteeing optimal mismatch error, secondly, (ii) a structured controller is designed using an H1-objective and coupled with an output saturation strategy to achieve flight performance and load clearance, i.e. wing root bending moment saturation. The entire procedure - i.e. approximation and control - is finally assessed on the high fidelity large-scale aircraft model, illustrating the effectiveness of the procedure on a complex model, used in the industrial context in the control laws validation process.

Keywords: Guidance, navigation and control of vehicles
Abstract: This paper describes a novel lateral guidance law of an unmanned aerial vehicle using model predictive control and shows its flight test results. The guidance law is accompanied with an extended Kalman filter which estimates steady wind velocities in order to follow a pre-specified reference path defined in the ground-fixed coordinate system. A small scale research vehicle, developed by Japan Aerospace Exploration Agency, is used for flight tests and the results show the proposed system's high guidance performance.

Keywords: Guidance, navigation and control of vehicles
Abstract: Landing on undisturbed surface of water is one of the most specific and complex elements of piloting seaplanes. The main reason for this is the complexity of visual assessment of altitude. Analysis showed that most of the existing methods and systems for contactless measurement of distance cannot be used for measuring flight altitude with required accuracy near water surface. Radio altimeters have the most suitable characteristics, however, for economic reasons, they are not installed on all seaplanes. Lack of accurate instrument information about the most important navigation parameter negatively affects the safety of flights. Above facts point to the importance of finding new methods of measuring flight altitude near undisturbed surface of water. For solving this problem, the paper focuses on analyzing the applicability of time-of-flight cameras, which are based on the Photonic Mixer Device (PMD) technology. The paper presents preliminary experimental results that confirm the possibility of using a static PMD camera for altitude measurement near undisturbed surface of water. Further, during landing the vertical velocity component and angular velocity of an aircraft do not have high values. The horizontal velocity component does not affect the view of range images in case of undisturbed water surface. Therefore an inference is made that PMD cameras can be used for measuring altitude of flight near undisturbed water surface.

Keywords: Logical design, physical design, and implementation of embedded computer systems
Abstract: Model driven engineering approaches can be used to handle the complexity in the development of modern mechatronic systems, containing a multitude of mechanical, electrical/electronic and software components. However, up to now SysML, as standard systems engineering language, is not wide spread in industry yet. Reasons therefore are missing adequate guidelines for the modeling process as well as an unclear benefit of the created SysML-models. A well-created system model however poses enormous time advantages during the analysis of change influences in later lifecycle phases of the system and makes an interdisciplinary reuse of modules in the development of new systems possible. A prerequisite therefore is the efficient traceability of all information within the system model. Thus, in this paper a SysML based process for the high-level development of mechatronic systems is applied, reaching from requirements specification to the detailed modeling of the element-connections (discipline specific as well as interdisciplinary). Our approach shows how the information from the different levels of abstraction and the different development phases can be connected, including a functional modularization of the mechatronic system. In this way, developers can trace change influences more easily. The functional modules can be used during the development of new systems, resulting in significant shortened development cycles. The proposed design pattern is shown at the example of a bench-scale model of a production plant.

Keywords: Programmable logic controllers, Embedded computer architectures, Logical design, physical design, and implementation of embedded computer systems
Abstract: Nowadays automation systems are required to be flexible in order to cope with the ever changing requirements of the applications in terms of complexity, extensibility or dynamism. The use of reconfiguration techniques helps to meet these demanding requirements, but at the same time increase the complexity of the systems. This paper presents a supervisor architecture that allows maintaining the availability of a control system in spite of failures such as a node crash, by means of reconfigurations at the control level. To do this, the advantages that Model-Driven Development technology provides have been explored.

Keywords: Intelligent manufacturing systems, Device integration technologies, Ontology-based models interoperability
Abstract: The traditional way of engineering production systems requires static information flows within and in between automation software, from field control software to manufacturing operations management. The common understanding of data and its containing information is realized by implicit assumptions on data semantics. In contrast, the vision of future production systems as cyber-physical systems (CPS) focuses on intelligent production facilities, which are characterized by autonomous behavior and dynamic, cooperative interactions. As a consequence, data and information that are stored and exchanged within CPS cannot rely on the assumption that other software systems are aware of data semantics. A means to solve this issue is ontologies - an approach being intensely discussed and applied for enhancing data with semantics. However, reuse of ontologies within the automation domain is hampered as ontologies are developed for specific use cases without having reusability in mind. In this paper, these drawbacks of ontology development are discussed and an approach for maximizing reusability through modularizing ontologies for different fields of the automation domain is presented.

Keywords: Logical design, physical design, and implementation of embedded computer systems, Remote and distributed control
Abstract: In the engineering of automated manufacturing systems a lot of effort is spent on the conceptual design and basic engineering phase. In this paper a methodology of modeling and evaluation of system concepts, based on mechatronic modules to support and ensure the decisions made during these phases, is presented. This methodology facilitates the design of such a system by integrating the reusable mechatronic modules in the engineering and simulation process. Therefore, the adaptation of engineering modules for their reuse within the simulation is described. The reuse of modules aims at reducing the time of modeling and evaluation of the resulting blueprints by machine simulation.

Keywords: Knowledge-based control, Fuzzy and neural systems relevant to control and identification
Abstract: Industrial systems are vulnerable to faults. Early and accurate detection and diagnosis in production systems can minimize down-time, increase the safety of the plant operation, and reduce manufacturing costs. Knowledge- and model-based approaches to automated fault detection and diagnosis have been demonstrated to be suitable for fault cause analysis within a broad range of industrial processes and research case studies. However, the implementation of these methods demands a complex and error-prone development phase, especially due to the extensive efforts required during the derivation of models and their respective validation. In an effort to reduce such modeling complexity, this paper presents a structured causal modeling approach to supporting the derivation of diagnostic models based on formalized process knowledge. The method described herein exploits the formalized process description guideline VDI/VDE 3682 to establish causal relations among key-process variables, develops an extension of the Signed Digraph (SDG) model combined with the use of fuzzy set theory to allow more accurate causality descriptions, and proposes a representation of the resulting diagnostic model in CAEX/AutomationML targeting dynamic data access, portability, and seamless information exchange.

Keywords: Logical design, physical design, and implementation of embedded computer systems, Embedded computer architectures
Abstract: The robotic-manipulator has, as aim, the integration of robots into people's quotidian issues. To this purpose, there are a great number of physical devices, such as sensors, actuators, auxiliary elements, tools… which can be incorporated into a robot. That is why integration, reuse, flexibility and adaptability are crucial characteristics demanded by current robotic applications. Using Model Driven Engineering (MDE) software development methodology that promotes an intensive use of models- systems’ abstractions-, the complexity inherent to the robotic application design can be reduced. This work explores the advantages of the use of (MDE) to provide support to development cycle of such type of applications. So, in this paper, a modeling approach, where the model is the key concept, is developed to generate automatically the target code. In addition, using this concept, the design of the robotic-arm application could be done independently of robotics’ communication middleware.

Keywords: Design of fault tolerant/reliable systems, Active approaches to fault tolerant control, Parameter estimation based methods for FDI
Abstract: In this article the industrial goals and objectives of the European Framework 7th project termed “REconfiguration of CONtrol in Flight for Integral Global Upset Recovery (RECONFIGURE)” are presented. Commercial aircraft fault tolerant control (FTC) strategies in the flight control system (FCS) are based on fail-safe flight control law reconfiguration which relies on upstream hardware redundancy-based robust Fault Detection and Diagnosis (FDD). This industrial state-of-practice fits well in the current certification process but it also decreases the easiness of the piloting task as soon as the system level of degradation increases. Thus, the main goal of RECONFIGURE is to research and develop aircraft FDD and FTC technologies that facilitate the automated handling of off-nominal/abnormal events and optimize the aircraft status and flight. The article details the project description of work, from industrial benchmark (fault scenarios and aircraft model) up to industrial verification and validation (V&V) activities, via advanced FDD/FTC research and development. The expected results and perspectives are also presented.

Keywords: Active approaches to fault tolerant control, Design of fault tolerant/reliable systems, Parameter estimation based methods for FDI
Abstract: This paper proposes a novel method for sensor allocation based fault tolerant control. Fault tolerance is achieved with optimal combination of healthy sensor sources while the baseline controller remains unchanged. The measurements are subjected to various sensor dynamics, hence the resulting sensor allocation framework is also dynamic. The proposed approach can fit into a hierarchical fault tolerant control framework, where certain sensor faults are handled by the lower level allocation while more severe faults are handled by controller reconfiguration. The decision of which reconfiguration level has to be initiated in response to a fault is determined by a supervisor unit. The method is demonstrated on the simulation model of the Nasa AirStar test vehicle.

Keywords: Observer based and parity space based methods for FDI, Statistical methods/signal analysis for FDI, Parameter estimation based methods for FDI
Abstract: This paper presents a novel moving horizon least-squares input estimation method for linear discrete-time stochastic systems. For systems with completely unknown initial state and no unstable zeros, some existing work showed that asymptotic input reconstruction is possible in the absence of noises. However, under the same condition but with stochastic noises, most existing input estimators, which are designed to optimally deal with noises, fail to ensure asymptotic unbiasedness. In order to address this limitation for linear discrete-time stochastic systems, we characterize necessary and sufficient conditions for input observability and detectability, and propose a moving horizon least-squares input estimator. Based on the conditions for input observability and detectability, it is proved that our proposed input estimator gives an asymptotically unbiased estimate and has minimal estimation error variance over all linear asymptotically unbiased input estimators. Its effectiveness is illustrated by simulation examples involving aircraft sensor and actuator faults.

Keywords: Active approaches to fault tolerant control, Observer based and parity space based methods for FDI, Design of fault tolerant/reliable systems
Abstract: An integrated fault diagnosis based fault tolerant longitudinal control system architecture is proposed for civil aircraft which can accommodate partial or total losses of angle of attack and/or calibrated airspeed sensors. A triplex sensor redundancy is assumed for the normal operation of the aircraft using a gain scheduled longitudinal normal control law. The fault isolation functionality is provided by a bank of 6 fault detection filters, which individually monitor each of the 6 sensors using robust low order LPV residual generators. In the case of losses of up to 5 sensors, a fault estimation technique based on LPV estimators can be employed to reconstruct the missing sensor information necessary for gain scheduling. In the worst case of a total failure of all 6 sensors, a robust constant longitudinal control law is employed which ensures a basic longitudinal control performance. The proposed control architecture fulfils the basic requirements formulated in the Benchmark Problem in the RECONFIGURE project.

Keywords: Observer based and parity space based methods for FDI, Design of fault tolerant/reliable systems
Abstract: This paper considers the problem of reconstructing, simultaneously occurring actuator and sensor faults in a nonlinear system. The theory which will be developed in this paper is based on a sliding mode observer formulation, designed around an LPV model approximation of the nonlinear system. It extends previous work which has independently considered actuator and sensor faults reconstruction. Here a single observer structure will be synthesized which can cope with simultaneous actuator and sensor faults. The paper will describe the development of the LPV model, the theory and synthesis of the sliding mode observer, and the results of applying this technique to the RECONFIGURE benchmark problem.

Keywords: End of life management, Sustainability, Reusability
Abstract: End of Life Management will be more and more important not only for electr(on)ic devices but also for automation devices. Therefore the main goal is to give an overview about the state of the art and further development trends in this field. In this paper, a first attempt for an evaluation model for finding optimal End-of-life strategies for specific devices is developed and presented. Based on this model, important devices within the modern automation technology are analyzed and ranked according to different criteria. The variety of the chosen factors shows, that it is very important to understand the products and the surrounding environment in detail. Special emphasis will be on new approaches for EoL of process- and manufacturing devices like automated extraction of rare materials from scrap, using electronic scrap for education in automation, economical aspects of disassembly with special emphasis to SME`s and developing countries.

Keywords: Manufacturing plant control, Dependable manufacturing systems control
Abstract: In this paper, the current situation of how PLC software is tested in industry is analyzed and the challenges on new testing approaches are identified using real industry code and a survey conducted within industry. The different possible and most relevant faults that may occur and must be dealt with are identified and requirements for testing approaches concerning component failures are derived. Further on, an approach to generate tests for error handling routines, which test the reliability of plants by injecting the corresponding faults is presented. The test cases are generated from timing sequence diagrams in combination with failure mode and effects analysis. In order to inject the faults at relevant points during the execution of the control software, IEC 61131-3 code is analyzed for the derivation of the test cases.

Keywords: Intelligent maintenance systems, Dependable manufacturing systems control, Process supervision
Abstract: This paper investigates an online effective method for tool condition monitoring. Acoustic emission signal of a system which is acquired by a sensor mounted to the spindle of the milling machining center is used as the fault indicator because it is easily to be installed, inexpensive and practical for use in industrial environment. Time-frequency analysis is selected for signal processing step based on its ability to reveal time and frequency variant characteristics of faulty signal. S-transform is used as a powerful time-frequency method for this purpose. Because of the high dimension of the time-frequency results, it is desirable to use a local region of interest in time-frequency domain instead of using the entire information, for fast and accurate monitoring and detection when any abnormal/fault operating condition might occur. Such a strategy also helps to reduce the computation cost which is necessary for online applications and improves the interpretation resolution for law quality signals. An optimization method based on genetic algorithm is used for finding the most discriminative local area as the region of interest in time-frequency domain. For feature generation step, a correlation coefficient between each signal and the healthy signal is assigned to the signal using a 2-D correlation analysis. Curve fitting approach is then used to determine a function to approximate the fault value based on the correlation coefficients. Experimental results based on a milling machine under different operating conditions show that this method has a high accuracy for fault detection. It is also concluded that the accuracy of the local feature extraction is higher than the conventional ways.

Keywords: Assembly and disassembly
Abstract: This paper deals with the problem of disassembly line balancing where partial disassembly and uncertainty of task times are studied. Few papers have addressed the stochastic disassembly line balancing problem and most of existing work focused on complete disassembly and have not considered AND/OR graphs. In the present work, tasks of the best selected disassembly alternative are to be assigned to a fixed number of workstations while respecting precedence and cycle time constraints. Task times are assumed to be random variables with known probability distributions. An AND/OR graph is used to model the disassembly alternatives and the precedence relationships among tasks and subassemblies. The objective is to balance workstations’ idle times, i.e. differences among stations’ loads are as small as possible. A stochastic binary program is developed. To illustrate the applicability of the solution method proposed, it was performed on on a set of problem instances from the literature.

Keywords: Life-cycle control, Maintenance models and services, Intelligent maintenance systems
Abstract: Asset Management is a relatively new concept being strongly promulgated world wide in organizations with large asset bases like transport, water, power, mines, large industry, etc. The benefits of holistic Asset Management in organizations are far reaching. The aim is to, in support of organizational goals, improve long term sustainability and performance, and to reduce risk and asset life cycle costs. It looks 50 years into the future and is about making the good decisions on what do to with assets and when to do it during their life cycles. Asset Management is of great and growing importance to organizations. This paper looks at areas where Automatic Control (IFAC) can contribute to Asset Management.

Keywords: Assitive technology and rehabilitation engineering, Robotics technology, Mechatronic systems
Abstract: This paper presents a hierarchical control scheme for a robotic transtibial prosthesis to realize smooth locomotion transitions between level ground and ramps. The high level controller identifies current terrain with a fuzzy logic based method and decides the corresponding parameters for lower level controllers. The middle level controller detects different gait phases of one gait cycle on a specific terrain and decides which control method to be used for the current phase. Based on the identified terrain and gait phase, the low level controller performs damping control for controlled plantarflexion/dorsiflexion, and angle control for the swing phase. To evaluate the effectiveness of the proposed control scheme, we design and construct a robotic transtibial prosthesis prototype. Experimental results on normalized stance time, normalized peak ground reaction force, and center of pressure shift in anterior/posterior show improved gait symmetry and walking stability of an amputee subject on ramps with the proposed control scheme. A 17-s long trial that includes different terrains and terrain transitions indicates that the proposed scheme can realize smooth locomotion transitions between level ground and ramps.

Keywords: Co-Learning and self-learning, Ajustable or adaptive autonomy, Intelligent robotics
Abstract: This paper presents a new technique for a reconfigurable exoskeleton which consists of a connected network of small sized holonic robots enwrapping the human body limbs and more specifically, in this paper, the human arm. The connection control of the reconfigurable exoskeleton decides upon the necessity of a ‘’disconnect’’,’’ reconnect’’ for chosen holons so as to balance any new load at the human joints. The intelligent controller carries out its decisions on the evaluation of stress at connections between holons of a previous configuration comparing them for a new actual need. This decision is based on a graph theoretic method, where stress calculations are carried out using finite element method. The paper demonstrates the performance of our approach on a simulation of hyper redundant network of holons wrapping a human arm.

Keywords: Intelligent interfaces, Modeling of human performance, Human operator support
Abstract: Human Adaptive Mechatronics (HAM) considers operator’s individual skills and preferences. Operator’s performance is time-varying and stochastic. Modifying the static parameters of the Human-Machine Interface (HMI) the human operator’s skill level and dynamic characteristics can be passively adapted. Knowledge-based approach is applied to change the machine parameters. This leads to a novel Skill Adaptive Control (SAC), which is described in the paper. A trolley crane system is simulated to test how operators can benefit of the SAC. Four operators were using the system.

Keywords: Modeling of HMS, Security and safety of HMS, Modeling of human performance
Abstract: The paper deals with unexpected behaviors and situations diagnosis for human-machine systems. Operator behavior is modeled using a nondeterministic discrete-event formalism and a specific model adaptation. An extension of pair algebra of partitions to nondeterministic finite state machines is used to develop the diagnosis method proposed in the paper. Possibilities of the proposed method are investigated and discussed. An illustrative example is provided, based on a tramway driving situation.

Keywords: Modeling of human performance, Telerobotics, Mobile robots
Abstract: The results from a user study on teleoperated steering tasks are used to create a driver model under different latency conditions. The 31-subject user study explored the effects of latency on teleoperated steering tasks using a simulated mobile robot receiving input commands from a teleoperator via a computer gamepad. Using fundamental concepts from automotive steering models, and examining the users' input commands to the simulated robot under different latency conditions, a model of a human teleoperator for steering tasks was developed, tuned and validated. The model is a PD controller with feedback based on the projected lateral displacement of the robot. The tuning of the model gains for different latency scenarios reflects the real-world control strategies that users must employ when adapting to system latency. Simulation results show that the control gains can be interpolated to predict teleoperator performance under latency scenarios that were not tested with users. An analysis of the closed-loop stability of the system confirms our empirical observation that the ratio of the controller gains Kd/Kp increases as the latency increases.

Keywords: Shared control, cooperation and degree of automation, Assitive technology and rehabilitation engineering, Human operator support
Abstract: There have been several design methodologies of impedance control without using force sensors, most of which use an observer to estimate external forces and conduct impedance control based on the observed external force. Since the external force is estimated in a feedback way in this type of force observer-based impedance control, it has been impossible to achieve reference tracking performance in these controllers due to high sensitivity function design. This paper proposes a novel frequency-shaped impedance control which can perform compliant motions and reference tracking. The proposed algorithm is based on the disturbance observer design which allows a straightforward design of compliant motion and employs feedforward controller to compensate for the response time that can be sacrificed due to the compliance design. Experiments with an industrial robot verifies the effectiveness of the proposed controller.

Keywords: Medical imaging and processing, Biomedical and medical image processing and systems, Healthcare management, disease control, critical care
Abstract: Post-contrastographic Dynamic Contrast Enhancement (DCE) is a consolidated MRI technique to perform non-invasive analysis via Imaging. The dynamics perfusion of specific drugs, named Contrast Agents (CA), allows to highlight anomalies in tissues, since the vascular substrate is differently developed (hypervascularized) for diseases compared with healthy parenchyma. The goodness of the analysis procedure influences the inferred diagnosis: the more objective the analysis, the more accurate the prognosis and the therapies follow-up. Deformations and displacements affect organs and soft tissues and their correction is challenging for DCE analysis; the complexity lies also on artefacts of image registration process because of the backscattered image signal intensity varies over time due to the diffusion/perfusion phenomenon induced by the CA injection. In this work we present a combined approach based on the Active Contours framework and on the Optimal Mass Transportation for the automatic depicting and tracking of deformable objects. Active Contours provide the automatical contour’s depiction of a deformable object by minimizing an energy cost functional; Optimal Mass Transportation allows to tracking a deformable object by minimizing the (object’s) transport cost under the (object) mass conservation principle. We applied these methodologies to post-contrastographic DCE MR images’ series and we are going to show the improvement in the goodness of DCE analysis compared with results gathered with other tracking strategies.

Keywords: Biomedical and medical image processing and systems, Medical imaging and processing, Biomedical system modeling, simulation and visualization
Abstract: The diaphragm is an important component of the respiratory pumping process. Orthogonal temporal sequences of MR images are acquired in free breathing without use of contrast. The vertical intersection between two orthogonal temporal sequences is determined, the diaphragmatic level in the vertical segment is determined and used to perform the temporal registration. Previous works used similar temporal registration algorithms to reconstruct the diaphragmatic surface. However, it is shown that the available information was not efficiently used. The proposed method massively performs temporal registrations, and then validates the determined temporal registrations. The images to be used in the diaphragmatic surface reconstruction are determined and its associated diaphragmatic levels are collected. Positions with inconsistencies have their diaphragmatic level determined by interpolation. The proposed algorithm is tested with MR image sequences obtained from a healthy subject.

Keywords: Bio-signals analysis and interpretation, Identification and validation, Bioinformatics
Abstract: This study addresses the classification problem of HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can indicate the presence of autoimmune diseases by searching for antibodies in the patient serum. Generally, IIF analysis remains a subjective method, which depends too heavily on the experience and expertise of the physician. Recently, some studies show that it is possible to identify the cell patterns using IIF by image analysis and machine learning techniques. However, it still has large gap in recognition rates to the physical experts' one. This paper explores the discriminative feature extraction of HEp-2 cell images in IIF, and then identifies the patterns of HEp-2 cell using machine learning techniques. Motivated by the research progress on computer vision that small local pixel pattern distributions can be highly discriminative, the proposed strategy employs a parametric probability process to model the local image patches (Textons: micro structure in the cell image), and extract the higher-order statistics (also called Fisher-Vector) to the model parameters for the image description. The proposed strategy can adaptively characterize the micro-Texton space of HEp-2 cell images as the generative probability model, and learn the parameters for better fitting the training space, which would lead to more discriminant representation for the cell image. The simple linear support vector machine is combined for cell pattern identification due to its low computational cost especially for large-scale dataset.Experiments on the released HEp-2 cell dataset of ICIP2013 competition validate that the proposed strategy can achieve much better performance than the popular used local binary pattern (LBP) image descriptor, and the achieved recognition error rate is even greatly below the observed intra-laboratory variability.

Keywords: Biomedical system modeling, simulation and visualization, Developments in measurement, signal processing, Intensive and chronic care or treatment
Abstract: Patient and nurse interaction in the Intensive Care Unit (ICU) is important as it influences the patient outcomes. Optimizing the nurse-to-patient ratio can reduce the mortality of patient, prevent nurse burnout, and reduce costs in the ICU. However, there is a lack of methods to quantify and evaluate these bedside interactions. This paper presents a Clinical Activity Tracking System (CATS), which was designed to track and evaluate nursing motion at the patient bedside, aiming to quantify how nurses spent their working time. CATS utilizes the Microsoft Kinect, a motion sensing device containing an embedded camera and infrared sensor. For CATS, the Kinect is fixed on the ceiling, facing downwards to track clinical activity at the patient bedside. The system was set up in an experimental environment to simulate the ICU bedside activity, and different motion paths and test candidates were tested over 5 iterations to evaluate the performance of the system. The total tracking area for the CATS can reach 2.3 m × 1.6 m, which mimics to the ICU bedside area. The system can track candidates with different heights from 1.52 m to 1.90 m. The system can also track different motion patterns consistently, with median percentage tracking error 2.30% (Inter-quartile range (IQR): [0.72%, 4.25%]). The system can also track multiple candidates with median percentage error 1.75% (Inter-quartile range (IQR): 0.97%, 4.57%). The results show that the system can be used in real-time applications to track bedside clinical activity. This system is capable of evaluating the ICU nursing activity, with the ultimate aim to generate appropriate nurse-to-patient ratio to prevent nurse burnout and increase patient care. Also, it is able to track different candidate heights, adapt to different motion paths, different dwell time, and identify multiple people simultaneously. The results revealed that the system can be used to quantify and evaluate bedside clinical activity.

Keywords: Tracer kinetic modeling using various imaging systems, Biomedical and medical image processing and systems, Medical imaging and processing
Abstract: In order to remain in a monotonic concentration-signal relationship, tracer with low concentration is usually administrated. A new tracer concentration quantification to determine the non-monotonic effect and to enable the correct interpretation of the MR signal intensity, is proposed. It is based on the evaluation of the MR signal intensity in time that might have multiple peaks. A new method for perfusion analysis is also proposed, that is based on the blood flow inside the vessels like the gamma variate model and the extravasation as the two compartment model. Samples of processing results of the proposed method applied to patients with confirmed cancer and pulmonary embolus are presented.

Keywords: Medical imaging and processing
Abstract: One of the largest application fields of the Single Photon Emission Computed Tomography (SPECT) technology is parallel imaging based myocardial perfusion scintigraphy. Any image improvements resulting from the evaluation of the distortions and noise phenomena have significant diagnostic value in this field. Several distortion effects have been compensated inherently - non-uniform photon abortion and non-linear distant dependent spatial resolution (DDSR) effects of the imaging - by our developed novel 3D iterative reconstruction method. However, equivocal hypo-perfusion segment have been detected around the apical region of the myocardium systematically on both mathematical phantoms, physical phantoms, and in human studies. We concluded that the Partial Volume Effect (PVE) phenomenon may cause the distortion. The aim of the current research is to discover the impact of the 3D parallel projection based reconstruction parameters on the apical lesion effect.

Several reconstruction setups with different parameter settings are executed using different mathematical phantoms to model the myocardium. The reconstructed images are compared with quantitative measures. The degree of apical lesion is decreased if a volume of 256^3 with voxel sizes of 2mm was applied instead of a volume of 128^3 with voxel sizes of 4mm. Higher resolution volumes caused reduced Partial Volume Effect. In addition, a lesser extent of detector blurring resulted in less apical lesion. Further investigations are planned to analyze relationship between reconstruction setups and apical lesion.

Keywords: Smart grids, Control of renewable energy resources, Instrumentation and control systems
Abstract: A library in the EcosimPro environment, developed with the objective of providing the tools needed to integrate MicroGrids into multi-domain simulations is presented. Thus, practical simulated components of renewable energy sources (photovoltaic arrays, wind turbines), diesel generators, power electronic interfaces (DC/AC or AC/DC/AC), energy storage devices (batteries) are developed, in order to integrate them with the existing libraries of loads (such as electrical systems, hydraulic systems, chemical engineering process, etc). Components in the library are designed so they can be parametrized from data sheet information by general engineers, and then graphically connected to simulate under variable conditions specific real-life Microgrids. With this, different configurations and control strategies for Microgrids can be tested when operating on the real process. After presenting the main ideas behind the dynamic modelling of the components, the application to a case study is presented.

Keywords: Power systems stability, Smart grids, Intelligent control of power systems
Abstract: In this paper is presented a semi-active fault-tolerant control (SAFTC) as an alternative to reconfigurable or self-repairing fault-tolerant architectures with application to power systems. The final goal is to achieve stability and a minimum level of system performance following the completely loss of the feedback signals in the controller, which in this case are remote wide-area signals. The proposed controller is designed under the framework of Linear Matrix Inequalities (LMIs) to achieve pole placement. The semi-active design is compared against and ordinary or non-fault-tolerant control design to highlight that following the loss of a wide-area signal in the control, the closed loop response can lead to instability in the non-fault-tolerant case. The results are validated through nonlinear simulations results using a reduced version of the Nordic power system where the effectiveness of the proposed approach is demonstrated.

Keywords: Distribution automation, Intelligent control of power systems, Modeling and simulation of power systems
Abstract: Social Evolutionary Programming algorithm based on a Social Cognitive Model is proposed in this paper. The uphill and downhill spinning reserves are introduced to cope with the power imbalances caused by wind fluctuation on the premise of full utilization of wind energy. The proposed algorithm has an advantage in the convergent stability and computational efficiency. Since climbing rates constraints have impacts on both start-up & shutdown schedules of generating units and the economic dispatch based on a fixed schedule, two different ways are put forward to calculate UC problem containing climbing rates constraints. Simulation results of a 10-unit system show that such a Social Evolutionary Programming algorithm can effectively cope with the change of reserve requirement due to the grid-connection of wind farm, and meet the multiple requirements of UC and dispatching.

Keywords: Smart grids, Intelligent control of power systems, Optimal operation and control of power systems
Abstract: This paper addresses the problem of controlling Heating Ventilation and Air Conditioning (HVAC) systems with the purpose of maintaining a desired thermal comfort level, whilst minimizing the electrical energy required. Using a pilot installation, in the University of Algarve, Portugal, a Model Based Predictive Control (MBPC) strategy is used to control the HVAC equipment. The thermal comfort is assessed using the predicted mean vote (PMV) index. The MBPC methodology uses predictive models, implemented by radial basis function neural networks, identified by means of a Multi-Objective Genetic Algorithm (MOGA). Experimental results show that this approach is feasible and robust, and able to obtain energy savings greater than 50%, under normal building occupation.

Keywords: Application of power electronics, Control system design
Abstract: The voltaic imbalance of cells will reduce supercapacitor stack storage eciency,and even cause permanent damage. To address this issue, a controllable voltage equalizer with state of charge prediction is proposed in this paper. First, the voltage equalizer is designed by using the controlled multiple-output dc-dc converter to provide a large equalization current. Then, a dynamic linear-recursive model is introduced to predict the state of each supercapacitor. Using the state of charge prediction, a control strategy is proposed to regulate the cells charging current. The proposed voltage equalizer can obtain the complete balance of capacitor voltage in large current applications. The simulation and experiment results validate the eciency of the proposed voltage equalizer.

Keywords: Modeling and simulation of power systems
Abstract: A new turbine model validation technique that is based on adaptation of look-up tables is described this paper. Simulation results from the VIDYN turbine simulation program and measurements on Big Glenn wind turbine, located outside Gothenburg, Sweden are used as an input to this new model validation technique. The models of the flapwise bending moment and power coefficient are validated for Big Glenn turbine. Measurement data are acquired during normal turbine operation. Verification results show good agreement between model outputs and measured data. The method allows prediction in a wide range of turbine operating variables, using only few measured points.

Keywords: Active approaches to fault tolerant control, Control system design, Parameter estimation based methods for FDI
Abstract: This paper proposes an approach to active fault tolerant control (AFTC) of a variable-speed wind turbine subject to actuator faults. The actuator faults in the turbine pitch system and generator system are considered in this work. Takagi-Sugeno (T-S) fuzzy modeling of the combined driver train, pitch and generator systems is proposed to account for nonlinearities in the dynamics. Moving horizon estimation (MHE) based on T-S fuzzy modeling is proposed as the actuator fault estimation unit. Model predictive control (MPC) based on T-S fuzzy modeling is used in the design of the AFTC unit in which the predictive controller compensates the actuator faults and takes into account the model nonlinearity and turbine system constraints. A T-S fuzzy observer is used to estimate system states and thus an output feedback strategy of an observer-based predictive controller is formed.

Keywords: Control of renewable energy resources, Optimal operation and control of power systems
Abstract: The contribution of this paper is the formulation of the wind farm power maximization problem as a multi-stage dynamic programming problem. This formulation is made possible by introducing a state-space model to describe the evolution of the wind velocity profile as a function of the free-stream wind velocity and the wind turbine control variables. This state-space model is coupled with a multi-stage utility function that quantifies the power to be maximized. The benefits of this approach include: a simple algorithm to determine the turbine optimal controls, as a feedback function of the wind farm state, and a rigorous, yet simple, method to calculate the limit of performance for wind farm power maximization. A one-dimensional cascade of wind turbines is used to illustrate the approach. Also given is an analytical expression for the maximum power that can be extracted from the one-dimensional cascade, which parallels the well-known Betz limit for a single turbine.

Keywords: Control of renewable energy resources, Modeling and simulation of power systems, Smart grids
Abstract: This work proposes an innovative control technique for improving the contribution to the grid frequency regulation provided by a set of wind power generators belonging to a wind farm. Models of individual generators and of the conventional grid primary frequency control are developed and used for designing a model predictive controller. A proper estimation algorithm is also introduced in order to provide both the dynamical state of the wind turbines and the actual local wind conditions to the regulator. The availability of this data makes the control algorithm able to improve the participation of the whole wind farm to the frequency regulation by suitably coordinating and differentiating the contribution of the individual generators. The proposed strategy is tested on a large wind farm using a dedicated real-time/real-data simulation environment.

Keywords: Modeling and simulation of power systems
Abstract: The power load data from neighboring cities are significantly correlated because they share the same hidden variables as well as the underlying noises. A multi-task Gaussian process method for non-stationary time series prediction is introduced and applied to the power load forecasting problem in this paper. The prediction accuracies are effectively improved due to the additional information provided by the related data sets. A novel algorithm for prediction is presented to reduce the computational complexity of the multi-task Gaussian Process method. We validate the algorithm's precision and efficiency on the real world short-time power load data sets.

Keywords: Smart grids, Optimal operation and control of power systems
Abstract: With increased attention of renewable energy, large-scale installation of photovoltaic (PV) generation and electricity storage is expected to be installed into the power system in Japan. In this situation, we need to keep supply-demand balance by systematically using traditional power generation systems as well as the PV generation and storage equipment. Toward this balancing, a number of prediction methods for PV generation and demand have been developed in literature. However, prediction-based balancing is not necessarily easy. This is because the prediction of PV generation and demand inevitably includes some uncertainty. Against this background, we formulate a problem to plan battery charge pattern while minimizing the fuel cost of generators with explicit consideration of prediction uncertainty. In this problem, given as interval quadratic programming, the prediction uncertainty is described as a parameter in constraint condition. Furthermore, we propose a method to find a solution to this problem from the viewpoint of monotonicity analysis. Finally, by numerical analysis based on this problem and its solution method, we discuss the relation between the minimal regulating capacity and the required battery charge/discharge pattern to tolerate a given amount of prediction uncertainty.

Keywords: Control of renewable energy resources, Model predictive and optimization-based control, Process modeling and identification
Abstract: The paper is motivated by making use of solar energy in public lighting services via an intermediate battery storage. The aim is to develop algorithms for controlling the energy flow in the system, in such a way that robustness against power outages is guaranteed and the total energy cost is minimized. A novel approach is proposed which predicts energy production and consumption by fitting stochastic models to historic data, and solves the resulting optimization problem on a rolling horizon. Experimental results are also presented, illustrating the behavior of the controlled energy system in typical winter and summer days.

Keywords: Modeling and simulation of power systems, Control of renewable energy resources
Abstract: The customer side operation is getting more complex and difficult in a smart grid environment because of the adoption of renewable resources, such as photovoltaic, wind turbine, geothermal and fuel cell and the use of energy storage systems. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. Photovoltaic is one of the common renewable energy resources in customer side. The output of photovoltaic is directly related to insolation and temperature at the installed location for a specific time. Therefore, obtaining precise weather information is critical for accurate estimation. However, the weather forecast information that customers can access is usually not precise and even not quantitative. In this paper, an irradiation prediction model for photovoltaic power generations based on limited weather forecast information is proposed. It is assumed that customers can obtain only ordinary daily weather forecast that usually provides the qualitative prediction of 3 hours unit for the next 24 or 48 hours. Some existing insolation estimation methods are combined and power conditioning system characteristics are considered. The model is applied to a field test site, and verified with historic data.

Keywords: Output feedback control, Asymptotic stabilization
Abstract: In this paper, we consider input-affine invertible MIMO nonlinear systems which can be transformed into a special normal form by means of the structure algorithm. The normal form highlights a partial state, a subset of state variables, which plays in this setting a role similar to that of the outputs and its derivatives in a SISO system. It is shown that, if a system in this class can be asymptotically stabilized by means of a static feedback from that partial state, then semiglobal stabilization can be achieved via dynamic feedback driven by the output of the system. The dynamic feedback in question is based a (non-trivial) extension to MIMO systems of the standard high-gain observer.

Keywords: Output regulation, Tracking, Generalized solutions of Hamilton-Jacobi equations
Abstract: Nonlinear regulation is the design of feedforward and feedback control laws that regulate the output of a nonlinear plant affected by a nonlinear exosystem. The standard approach involves two steps. First calculate a feedforward control law that achieves regulation when the states of the plant and exosystem are on a given manifold, called the tracking manifold. In discrete time this requires the off-line solution of the Francis-Byrnes-Isidori (FBI) equations. The second step is to design an additional feedforward and feedback control law that drives the combined system to the tracking manifold. This second step can be accomplished by soving off-line the dynamic programming equations (DP) for an infinite horizon optimal control problem. Solving either the FBI or the DP equations is difficult when state dimensions are greater than one. This paper introduces the Model Prediction Regulation (MPR) method for nonlinear regulation. We show that by choosing a suitable running cost, regulation can be achieved by MPR type methods, i.e., solving on-line finite horizon nonlinear programs. We also show how to approximate the ideal terminal cost for the nonlinear program.

Keywords: Output feedback control, Robustness analysis, Analytic design
Abstract: This work presents an extension of a design procedure for dynamic output feedback design for systems with nonlinearities satisfying quadratic constraints.

In this work we used an axial gas compressor model described by the 3-state Moore-Greitzer compressor model (MG) that has some challenges for output feedback control design (Planovsky and Nikolaev 1990), (Rubanova 2013).

The more general constraints for the investigation of the robustness with respect to parametric uncertainties and measurement noise are shown.

Keywords: Output feedback control, Switching stability and control, Stability of nonlinear systems
Abstract: This paper is about the bang-bang control strategy for a class of nonlinear stochastic systems. The goal is to give a new approach to stabilize the considered systems which are affected by multiplicative noises by using a state feedback control at first, then an observer-based feedback control in a second time. The so called bang-bang controller permits to ensure the almost sure exponential stability of the closed loop stochastic system.

Keywords: Output feedback control, Energy systems, Application of nonlinear analysis and design
Abstract: The next experimental step in the development of nuclear fusion reactors is the ITER tokamak. It is designed to explore the burning plasma regime in which the plasma temperature is sustained mostly by self-heating from fusion reactions. Burn control, the control of fusion power and other reactor parameters through modulation of fueling and heating, will be essential for achieving and maintaining desired operating points and ensuring stability. Design of burn control strategies is made challenging by the multi-variable, highly nonlinear, uncertain nature of the system. Furthermore, due to the extreme conditions in fusion reactors, diagnostic systems may be limited. To deal with these challenges, we propose the use of a nonlinear, multi-variable output feedback control strategy with a proportional-integral observer. A simulation study shows the performance of the scheme using a set of diagnostics likely to be available in ITER.

Keywords: Output feedback control, Robust control, Lyapunov methods
Abstract: The global asymptotic stabilization problem for a general class of uncertain nonlinear systems with dynamic input uncertainties (input unmodeled dynamics) is considered. The system structure addressed in this paper is comprised of a nominal system (which can have a general nonlinear dynamics structure and is assumed to have a nominal control law to stabilize the nominal system) and an appended input unmodeled dynamics. The proposed approach is based on a control redesign utilizing the nominal control law and incorporating a singular-perturbation-like dynamic system extension along with a dynamic scaling to address the dynamic input uncertainties. The control input uncertainty structure is modeled through an uncertain non-affine function of the control input and the unmeasured state of an uncertain input unmodeled dynamics subsystem. The proposed control redesign approach is applicable to a wide class of nonlinear systems including triangular as well as non-triangular system structures as long as a set of structural and inequality conditions on the system dynamics are satisfied and provides a global robust output-feedback stabilizing controller.

Keywords: Continuous time system estimation, Filtering and smoothing
Abstract: This paper considers a continuous-discrete (CD) nonlinear filtering problem in the framework of Gaussian filters. After reviewing the equivalent linearization of static nonlinearities, we derive the continuous-discrete exact Gaussian filter (CD-ExGF) and equivalent linearization Kalman filter (CD-EqKF). It is shown that two CD nonlinear filters have the same time update differential equations for the conditional mean and covariance matrix, and that a difference is in the Kalman gain for the measurement update. Numerical methods of integrating a stochastic differential equation and time update differential equations are developed using the Heun scheme. Results of two simulation studies are included to show the difference and similarity of the CD-EqKF,CD-ExGF and CD extended-Kalman filter (CD-EKF).

Keywords: Continuous time system estimation, Nonparametric methods, Mechanical and aerospace estimation
Abstract: This paper analyzes finite dimensional linear time-invariant systems with observation of a delay, where that delay satisfies a particular implicit relation with the state variables, rendering the entire problem nonlinear. The objective is to retrieve the state variables from the measured delay. The first contribution involves the direct inversion of the delay, the second the design of a finite dimensional observer, and the third involves the derivation of certain properties of the delay - state relation. Realistic examples treat vehicles with ultrasonic position sensors.

Keywords: Continuous time system estimation, Estimation and filtering, Synthesis of stochastic systems
Abstract: Stochastic dynamical systems are fundamental in state estimation, system identification and control. System models are often provided in continuous time, while a major part of the applied theory is developed for discrete-time systems. Discretization of continuous-time models is hence fundamental. We present a novel algorithm using a combination of Lyapunov equations and analytical solutions, enabling efficient implementation in software. The proposed method circumvents numerical problems exhibited by standard algorithms in the literature. Both theoretical and simulation results are provided.

Keywords: Continuous time system estimation
Abstract: In this paper, we extend the modulating functions method to estimate the state and the unknown input of a linear time-varying system defined by a linear differential equation. We first estimate the unknown input by taking a truncated Jacobi orthogonal series expansion with unknown coefficients which can be estimated by the modulating functions method. Then, we estimate the state by using extended modulating functions and the estimated input. Both input and state estimators are given by exact integral formulae involving modulating functions and the noisy output. Hence, estimations at different instants can be non-asymptotically obtained using a sliding window of finite length. Numerical results are given to show the accuracy and the robustness of the proposed estimators against corrupting noises.

Keywords: Continuous time system estimation, Grey box modelling, Software for system identification
Abstract: Grey-box models give us a welcomed opportunity to combine our prior knowledge with experimental data when a system is being identified. This benefit is redeemed by an unattractive non-convex and nonlinear optimisation problem that ensue from the parameter estimation. The article shows an efficient method how to speed up the arising iterative optimisation algorithm in the case of continuous, time-invariant, grey-box models. The method is based on a presented output-to-parameter sensitivity computation algorithm.

Keywords: Continuous time system estimation, Estimation and filtering, Filtering and smoothing
Abstract: This paper introduces a new filter for linear continuous-time stochastic systems with delayed measurements. The approach is inspired by an observer designed for deterministic systems. The obtained solution is suboptimal and does not use distributed integration terms with advantages in terms of computational load. The relationship between the delay bound and the variance of the estimation error is formally characterized and confirmed by a numerical example.

Keywords: Control under communication constraints, Control and estimation with data loss, Quantized systems
Abstract: In this paper, we deal with the stabilizability of a scalar plant through a noisy discrete channel. Our scheme can be regarded as a variation of the controllers based on dynamic quantizers introduced in [3] and [4], but our analysis based on Lyapunov function allows us to take into consideration more general discrete memoryless channels. Given a plant and a discrete memoryless channel we will provide, using control Lyapunov function ideas, a condition for the existence of an encoder/controller pair with a priori xed memory that achieves stability. A construction procedure is also provided.

Keywords: Control under communication constraints, Control over networks
Abstract: This paper investigates controllability of discrete-time Networked Control Systems. The distinguishing feature is that the network imposes scheduling. The network is characterized by a dynamic protocol and different types of additional processing capabilities, as determined by available technology. For NCS with general nonlinear plants we present general controllability results. Finally, for NCS with linear plants we extend ideas motivated by NCS architectures with static protocols to state corresponding controllability results.

Keywords: Control over networks, Control under communication constraints
Abstract: The switched model predictive control (MPC) strategy is investigated in this paper for a class of networked control systems (NCSs) with time delays and packet disordering. A new model is proposed to describe the NCS with packet disordering. State feedback controllers are considered and the resulting closed-loop NCS is modeled as a discrete-time switched system. By using the average dwell-time approach and switching between different cost functions, sufficient conditions for exponential stability of the NCS and design procedures for the stabilizing controllers are presented. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.

Keywords: Control under communication constraints, Control over networks, Networked embedded control systems
Abstract: This paper aims at developing Bode-type integrals for control systems over additive white Gaussian noise channels, by way of deriving information theoretic equalities and inequalities. The integrals characterize the fundamental performance trade-offs of such networked feedback systems with linear time-invariant plants and causal stabilizing controllers. We propose two new notions to facilitate our development: channel blurredness and negentropy rate. The channel blurredness provides an alternative measure for the quality of communication channels to the conventional notion of channel capacity. The negentropy rate, on the other hand, relates the entropy rate of a stochastic process to its power spectrum. Both notions are shown to be closely relevant to networked feedback systems. Indeed, the Bode-type integrals developed herein are seen to depend on the channel blurredness of the communication channel, as well as the negentropy of the exogenous disturbances.

Keywords: Control over networks, Control and estimation with data loss, Stochastic control and game theory
Abstract: In networked control systems, sequence-based controllers are used to compensate for transmission delays and losses in unreliable data networks. For this purpose, the controller not only sends the current control input to the actuator but also a sequence of predicted control inputs. The additional inputs can be used when subsequent transmissions get delayed or lost. In this paper, the sequence-based method is applied to the problem of trajectory tracking over an unreliable network and an optimal sequenced-based tracking controller is derived. The main advantage of the presented approach is that future information on the reference trajectory can optimally be embedded in the predicted control sequences. Furthermore, the controller can be implemented offline. An interesting result is that the optimal controller can still be separated into a feedback part and a feedforward part (as in standard optimal tracking control) despite of both the unreliable network and the sequence-based method. The performance of the derived tracking controller is demonstrated by Monte Carlo simulations with an inverted pendulum.

Keywords: Control of networks, Control over networks, Complex system management
Abstract: Controllability and observability have long been recognized as fundamental structural properties of dynamical systems, but have recently seen renewed interest in the context of large, complex networks of dynamical systems. A basic problem is sensor and actuator placement: choose a subset from a finite set of possible placements to optimize some real-valued controllability and observability metrics of the network. Surprisingly little is known about the structure of such combinatorial optimization problems. In this paper, we show that an important class of metrics based on the controllability and observability Gramians has a strong structural property that allows efficient global optimization: the mapping from possible placements to the trace of the associated Gramian is a modular set function. We illustrate the results via placement of power electronic actuators in a model of the European power grid.

Keywords: Positive systems, Time-invariant systems, Linear systems
Abstract: This paper addresses the formation control problem of multi-agent systems whose dynamics are all positive. Recently, as a byproduct of the analysis of interconnected positive systems, we have shown an effective way for designing a communication scheme (i.e., an interconnection matrix) over the positive agents so that a prescribed formation can be achieved. For the convergence rate analysis of such multi-agent positive systems under formation control, we propose an efficient algorithm to compute the dominant pole of interconnected positive systems by actively using the positive property of each agent. We illustrate by numerical examples that the proposed algorithm is definitely efficient particularly when the number of agents gets larger.

Keywords: Time-invariant systems, Positive systems
Abstract: Studies of distinguishability have focused on the case of Linear Time-Invariant systems without uncertainties. In this work, distinguishability is studied for Positive Linear Time-Invariant systems with affine parametric uncertainties in the state space model. We propose a denition of distinguishability adapted to this new context and give a characterization of this notion. The approach used is based on the estimate of the reachable output space of the systems. Under suitable assumptions, a sufficient condition for distinguishability is established

Keywords: Positive systems, Structural properties, Linear systems
Abstract: Positive reachability and positive observability of positive linear systems on time scales are studied. Differential and integral calculus on time scales allows for unified treatment of continuous- and discrete-time systems. Reachability and observability graphs for positive systems are introduced. They are used to characterize positive reachability and positive observability for systems on homogeneous time scales. It is also shown that positive observability is dual to positive reachability. Positive minimality is introduced and characterized with the aid of Gram matrices, Hankel matrices and graphs.

Keywords: Positive systems, Model reduction, Relaxations
Abstract: In this paper, we investigate model reduction for positive linear time-invariant systems in the H-infinity sense. First, the bounded real lemma is further developed to obtain a new characterization of the H-infinity performance, which does not include any product term between the Lyapunov matrix and the parameter of the reduced-order model. Then a new parameterization of a positive reduced-order model is proposed, and accordingly, an iterative algorithm is constructed, which makes use of the coarse reduced-order models resulting from the (generalized) balanced truncation as the initial value to search for a desired positive one. Both continuous- and discrete-time systems are considered in the same framework. Numerical examples clearly show the effectiveness and advantages of the proposed method.

Keywords: Linear systems, Digital implementation
Abstract: In this paper, some theoretical results on controllability, observability and duality for isolated time scales systems are given. It is also shown that eigenvalue assignment via a state feedback (output injection) controller on isolated time scales yields desired system behavior of a continuous system. Furthermore, a method for designing such a controller is introduced.

Keywords: Linear systems, Output feedback control
Abstract: The paper deals with tracking problem for single-input single-output linear systems with unstable zero dynamics under parametric and signal uncertainty of control plant and reference model within the framework of the sliding mode techniques for feedback design, state observation and parameters identification in real time.

Keywords: Motion Control Systems, Mobile robots, Autonomous robotic systems
Abstract: A robust solution to the trajectory tracking control problem for a nonholonomic wheeled mobile robot should deal with the existence of structural and parameter uncertainties, external disturbances and operating limitations. The first order sliding control with boundary layer is a common and suitable solution that can ensure chattering attenuation, but with poor degree of robustness. Fortunately, higher order sliding mode control can achieves greater degree of robustness with the reduction of the chattering phenomenon. Based on this knowledge, a control strategy is proposed using a super-twisting sliding mode control, which enforces a second order sliding mode, in cascade with an inverse dynamic control with proportional plus derivative control to solve the problem achieving good robustness. This linear control technique plays an important role in increasing the robustness by mitigating the influence of neglected dynamics. Experimental results are explored to show the effectiveness of the proposed strategy.

Keywords: Motion Control Systems, Mobile robots, Robots manipulators
Abstract: The work focuses on the state feedback synthesis of hybrid mechanical systems under unilateral constraints. The problem of robust control of mechanical systems is addressed under unilateral constraints by designing a nonlinear state feedback H_infinity-controller developed in the hybrid setting, covering impact phenomena. Performance issues of the developed nonlinear H_infinity-tracking controller are illustrated with numerical tests on a seven-link biped robot.

Keywords: Motion Control Systems, Modeling, Flying robots
Abstract: This paper describes the modeling and control of a birotor aircraft with tiltrotor configuration for the path tracking problem. The equations of motion of the tilrotor UAV is derived using Euler-Lagrange formulation, resulting in a nonlinear eight degrees-of-freedom dynamic model. To perform the control design, a linearized error model is obtained from the nonlinear model, which is used to synthesize a H-infinity and a multiobjective H_2/H-infinity controllers by using LMI approach. Simulation results for an ellipsoidal trajectory with persistent disturbances are carried out to corroborate and compare both controllers.

Keywords: Motion Control Systems, Robotics technology, Autonomous robotic systems
Abstract: This paper deals with multi-axis motion planning, especially with time synchronization of mutually independent axes of motions (uncoordinated motion). A novel time-optimal generator called GBAVS is used as a basic tool, utilizing the chain of integrators. The structure of generator uses 3 integrators for generating motion variables. The task is to plan the single-axis motions while respecting hard motion constraints and to achieve the final states in the same time. In contrast to other approaches the initial/final state does not necessary mean the state with zero velocity and acceleration, so called rest-to-rest motion. To reach synchronization, time scaling is based on decreasing the individual values of Jerk for each axis except the slowest one. Jerk is a parameter to be reduced as first. No initial/final state limitation and reduction of the value of Jerk primal to other motion limitations (e.g. velocity limits) are the main advantages of this approach.

Keywords: Motion Control Systems, Robots manipulators
Abstract: This paper presents a new approach for the energy-efficient trajectory planning of a mobile agent with obstacle avoidance. The motion of the mobile agent is subject to position constraints characterizing an obstacle (keep-out region) as well as velocity, acceleration, and control constraints. The original optimal control problem is transformed into a mathematical programming problem where the obstacle is described by a set of linear constraints and switching times, which specify the sequence of active constraints corresponding to the obstacle. A two-stages decomposition method is proposed to solve the optimal control inputs and switch times and is verified through simulations. The proposed approach can be applied to solve general obstacle avoidance trajectory planning problems.

Keywords: Motion Control Systems, Robots manipulators, Intelligent controllers
Abstract: This paper presents a robust tracking controller equipped with a fast friction estimator for a 6-DOF parallel electrical manipulator in the joint-task space. Parallel manipulator control scheme mainly stems from two frameworks: task space control and joint space control. The former requires the system states obtained via costly direct measurements or time-consuming forward kinematics. The latter's performance is subject to the inaccurate dynamic model. To minimize their weaknesses, the dynamics is represented in joint-task space by transforming the dynamics on the task space into joint space. With this transformation, the desired positional data are used to calculate the nominal value of dynamics. On the other hand, the friction property depends on the uncertain load condition due to the spiral drive way of electrical cylinders. A fast friction estimator is then designed to estimate the uncertain friction fast and effectively. The robust control scheme is proposed based on the Lyapunov design method to guarantee a practical stability under uncertainties such as inertia, modeling error, friction, and measurement errors. Experimental results are presented to show the effectiveness of the proposed scheme.

Keywords: Identification and validation, Healthcare management, disease control, critical care, Tracer kinetic modeling using various imaging systems
Abstract: The present paper proposes a computer vision system to diagnose the stage of illness in patients a ected by Alzheimer's disease. In the context of Ambient Assisted Living (AAL), the system monitors people in home environment during daily personal care activities. The aim is to evaluate the dementia stage, observing actions listed in the Direct Assessment of Funcional Status (DAFS) index and detecting anomalies during the performance, in order to assign a score explaining if the action is correct or not. In this work brushing teeth and grooming hair by a hairbrush are analysed. The technology consists of the application of a Recurrent Neural Network with Parametric Bias (RNNPB) that is able to learn movements connected with a speci c action and recognize human activities by parametric bias that work like mirror neurons. This study has been conducted using Microsoft Kinect to collect data about the actions observed and oversee the user tracking and gesture recognition. Experiments prove that the proposed computer vision system can learn and recognize complex human activities and evaluates DAFS score.

Keywords: Identification and validation, Quantification of physiological parameters for diagnosis and treatment assessment, Biomedical system modeling, simulation and visualization
Abstract: A simple experimentally validated cardiovascular system model has been shown to be able to track the evolution of various diseases. The model has previously been made patient-specific by adjustment of its parameters on the basis of a minimal set of hemodynamic measurements. However, this model has not yet been shown to be structurally identifiable, which means that the adjusted model parameters may not be unique. The model equations were manipulated to show that, from a theoretical point of view, all of their parameters can be exactly retrieved from a restricted set of model outputs. However, this set of model outputs is still too large for a clinical application, because it includes left and right ventricular pressures. Consequently, further hypotheses that determine some model parameter values have to be made for the model to be clinically applicable.

Keywords: Developments in measurement, signal processing, Quantification of physiological parameters for diagnosis and treatment assessment, Clinical trial
Abstract: Mechanical ventilation (MV) is one of the most common, but difficult, costly and variably delivered therapies in intensive care. Model-based estimated patient-specific respiratory elastance can be used to guide clinical staff in selecting positive end-expiratory pressure (PEEP) for MV patients. The Clinical Utilisation of Respiratory Elastance (CURE) trial investigates the potential to optimise PEEP in MV patients. A software system, CURE Soft, capable of providing real-time patient-specific respiratory elastance was developed for the purpose of CURE clinical trial. CURE Soft uses airway pressure and flow data from the ventilator to calculate respiratory elastance using a time-varying elastance model. The CURE Soft graphical user interface (GUI) was developed to provide patient-specific respiratory elastance in real time, history and response during a recruitment manoeuvre both of which offer significant clinical insight. CURE Soft was extensively tested on a mechanical test lung to verify the model-based elastance estimation and its robustness. Model fitting errors are low across a range of ventilation modes, showing it can successfully identify respiratory elastance. Real-time monitoring of respiratory elastance enables new insights into patient-specific lung condition, with no added patient burden. The additional insight available to clinicians will provide the information necessary for improved decision-making and patient outcomes.

Keywords: Model formulation, experiment design, Identification and validation, Medical imaging and processing
Abstract: The three-parameter Rayleigh damping (RD) model applied to time-harmonic Magnetic Resonance Elastography (MRE) has potential to better characterise fluid-saturated tissue systems. However, it is not uniquely identifiable at a single frequency. One solution to this problem involves simultaneous inverse problem solution of multiple input frequencies over a broad range. As data is often limited, an alternative elegant solution is a parametric RD reconstruction, where one of the RD parameters (μ_I or ρ_I) is globally constrained allowing accurate identification of the remaining two RD parameters. This research examines this parametric inversion approach as applied to textit{in vivo} brain imaging.

Overall, success was achieved in reconstruction of the real shear modulus μ_R that showed good correlation with brain anatomical structures. The mean and standard deviation shear stiffness values of the white and gray matter were found to be 3 ± 0.11 kPa and 2.2 ± 0.11 kPa, respectively. Parametric results with globally constrained μ_I indicate that selecting a reasonable value for the μ_I distribution has a major effect on the reconstructed ρ_I image and concomitant damping ratio (ξ_d). More specifically, the reconstructed ρ_I image using a realistic μ_I=333 Pa value representative of a greater portion of the brain tissue showed more accurate differentiation of the ventricles within the intracranial matter compared to μ_I=1000 Pa, and ξ_d reconstruction with μ_I= 330 Pa accurately captured the higher damping levels expected within the vicinity of the ventricles.

Parametric RD reconstruction shows potential for accurate recovery of the stiffness characteristics and overall damping profile of the in vivo living brain despite its underlying limitations.

Keywords: Medical imaging and processing, Biomedical and medical image processing and systems
Abstract: High-frequency noise is present in magnetic resonance images and it is usually removed by a filtering process. The anisotropic diffusion filter (ADF) was proposed to adaptively remove the noise, maintaining the image edges. However, as quantified in this paper for the first time, ADF methods still produce unsatisfactory results. While previous ADF implementations used the gradient of the strongest edges or the standard deviation of a planar region (i.e. without any edges), it is proposed a novel approach with improved parameter estimation based on both edge and planar region, overcoming some of ADF important limitations. Our framework was validated in more than thirty magnetic resonance images leading to more consistent results.

Keywords: Identification and validation, Biomedical system modeling, simulation and visualization, Physiological Model
Abstract: Gradient descent parameter identification methods are typically effective for objective surfaces that are well conditioned, but can result in excessive computational cost or failed convergence when this is not the case. This research presents a novel Regional Conjugate Optimisation (RCO) approach, for parameter identification in three dimensions. The method characterises the objective surface prior to the iterative process, which improves the ability of the method to correctly determine key features of the objective surface and exploit these characteristics during iteration. The RCO method is validated using a Monte-Carlo methodology on a series of contrived objective surfaces, and compared to the Levenberg-Marquardt (LMQ) gradient descent method. The RCO method demonstrated faster convergence in 96% of the tested cases, demonstrating the potential of this method to result in faster convergence, higher accuracy, and lower computational cost for certain classes of problems.

Keywords: Electric and solar vehicles, Neural networks, Automotive system identification and modelling
Abstract: The energy storage system (ESS) plays a significant role in fulfilling the driving performance requirements and ensuring operational safety in an electric vehicle. Ultracapacitors (UCs) are being actively studied and used in parallel with batteries or fuel cells forming hybrid energy storage systems in electric vehicles. They show excellent potential in terms of the sourcing and sinking of power, particularly for the peak-power demand encountered in aggressive regenerative braking. Since there are an increasing number of ultracapacitor applications, which now includes commercial automotive applications, establishing a good model to represent their dynamics, especially the residual capacity estimation (RCE), is vital; but this is challenging. This paper presents a residual capacity estimation model which is based on an artificial neural network (ANN). This takes both charging and discharging current and temperature into consideration. The proposed ANN model comprises of three inputs and one output: the inputs are temperature, current and voltage, and the output is the residual charge. The model is trained and validated by feeding a test database which is extracted from experimental testing of ultracapacitors at different currents and temperatures on a well-established test rig. The training data should span the whole prediction scope, therefore the test currents and temperatures both vary over a wide range and cover all the possible operational conditions of the on-board ultracapacitors. The Back-Propagation (BP) algorithm, together with an early stopping strategy, is adopted to train the proposed ANN model to assure adequately accurate prediction while avoiding overfitting risks. The model performance is validated with experimental results over a set of test data randomly selected.

Keywords: Electric and solar vehicles, Adaptive and robust control of automotive systems, Control architectures in automotive control
Abstract: This paper deals with a control strategy used for designing energy management systems within average-power electric vehicles. The power supply system is composed of three sources, namely a fuel cell, a battery and an ultracapacitor - specialized within distinct frequency ranges - which must be coordinated in order to satisfy power demand of the vehicle's electrical motor. The three sources with their associated DC-DC converters are paralleled on a common DC-bus supplying the electrical motor. The DC-bus is required to be constant regardless of the load state thanks to the fuel cell which provides the mean power and to the other two sources - auxiliary sources - which are controlled to supply the high-frequency variations of power demand according to an Hinf optimization strategy. MATLAB/Simulink numerical simulation is used to validate the proposed strategy under real driving cycle condition proposed by IFSTTAR (Institut Francais des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux), and this approach is assessed against another optimal strategy that uses LQR as control design.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Electric and solar vehicles, Automotive system identification and modelling
Abstract: The fast charge battery control problem is characterized by the need to have a sufficiently detailed model that can capture both the charging process and the inevitable constraints that limit the rate of charging due to battery state of health requirements. Presently, it appears the minimal modeling requirements to address the charging problem in model based fashion are unknown. This work seeks to develop a modeling methodology covering a large range of applications through systematically simplifying the partial differential equations that describe battery dynamics. The effects of grid resolution and polynomial order on the complexity and accuracy of reduced order models have been investigated to provide insight into the minimum modeling requirements at different charge rates. The proposed models are intended for controller design and optimization applications including fast charge control.

Keywords: Vehicle dynamic systems, Electric and solar vehicles, Trajectory Tracking and Path Following
Abstract: Autonomous vehicles are becoming a reality that in the next future will most probably start populating everyday roads. Such vehicles can, on the one hand, increase safety through automated driving, and, on the other, be a means of transportation also for people with disabilities who cannot move alone on commercial cars. Within this class of vehicles, mechanical layouts that allow an actuator redundancy coupled with electric propulsion appear particularly interesting, as they make it possible to design motion controller that can optimally blend multiple objectives, both dynamic, safety and driver-oriented. This paper considers such setting and concentrates on the design of a path-following algorithm with minimum-time features, with the aim of combining performance and energy-oriented optimization of the vehicle motion. The effectiveness of the approach is assessed by means of simulation tests carried out on the CarSim vehicle simulation environment.

Keywords: System integration and supervision, Electric and solar vehicles, Hybrid and alternative drive vehicles
Abstract: Large lithium-ion battery systems for electric vehicle and stationary storage applications use from tens to hundreds of series-connected cells. In this paper we present an advanced integrated management structure and method that uses intelligent switching to balance series-connected cells. Our analysis shows that using this approach can increase the useful system capacity by upwards of 7% compared to conventional active or passive balancing systems by fully utilising cells with different capacities. The battery system efficiency in an electric vehicle has also been shown to increase by up to 3% compared to conventional systems. Further advantages of integrated balancing include simultaneous integration of both primary and secondary power loads or sources, as well as robustness to individual cell failure. This indicates significant potential for implementation in future lithium-ion battery packs.

Keywords: Modeling and simulation of transportation systems, Automatic control, optimization, real-time operations in transportation, Modeling, supervision, control and diagnosis of automotive systems
Abstract: Conventionally, all auxiliaries present in a heavy-duty vehicle (e.g., power-steering pump, air-conditioning compressor) are engine-driven systems, which put high constraints on their performance. Outputs (e.g., speed, temperature) and energy consumption are dictated by engine speed, while most auxiliary demands are not proportional to the engine speed. Dealing with worst-case scenarios leads to highly oversized components that further, dramatically reduce the overall efficiency. How to choose, in a simultaneous design step, a topology, component sizes and a control algorithm for auxiliaries is still unknown. This becomes, especially, important when an integrated general optimal design is desired for the vehicle rather than an optimal system or sub-system design. To overcome the drawbacks of a sequential design approach, this paper shows the precise combination of technology, topology, size and control for the power steering system used in a heavy-duty vehicle. Modeling of six possible topologies and optimal sizing of components, as the gear ratio between combustion engine and power steering pump, are shown. Next, a sensitivity analysis is done for control parameters and a view is presented on a suitable topology for a power steering system used in a heavy-duty long-haul vehicle.

Keywords: Task allocation-sharing and job design, Human operator support, Guidance navigation and control
Abstract: This paper develops a novel multi-objective optimisation method based on the Evolutionary Game Theory to solve Weapon Target Assignment problems in real-time. The main research question of this study was how to consider multi-objective functions all together and choose a best solution among many possible non-dominant optimal solutions. The key idea is the best solution can be considered as a solution which best survives in other solution spaces. Therefore, the proposed method first obtains individual solutions for each objective function. Then, Evolutionary Game Theory considers each solution as a player and evaluates them in the solution spaces of other players to check how they can survive in those spaces. The main innovation is that, unlike other multi-objective optimisation approaches, the proposed approach not only considers a set of optimal solutions regarding multi-objective functions, but also finds the best optimal solution in terms of the survivability. The stability and the real-time computation of the proposed algorithm is tested on an adapted and constrained Dynamic Weapon Target Assignment problem matching a real military requirement. The performance of the proposed approach is evaluated via numerical simulations.

Keywords: Guidance navigation and control
Abstract: Full-state single-loop and full-state two-loop autopilot-guidance designs are derived using a linear quadratic differential game formulation. To keep all the states at reasonable values throughout the end-game, a cost function that includes appropriate running cost terms on some of the states is proposed. It is proven that the two designs may be identical under a linear quadratic differential game formulation and the proposed cost function. The theorem and guidance laws are illustrated using an interceptor missile having forward and aft controls.

Keywords: Guidance navigation and control, Flying robots
Abstract: This paper introduces a new approach to guidance law design using linear quadratic optimal control theory, minimizing throughout the engagement the variation of the control input as well as the integral control effort. The guidance law is derived for arbitrary order missile dynamics and target maneuvers. Explicit results are provided for a first order missile model and a constant target maneuver. It is shown that in the limiting cases the guidance law degenerates to either Cottrell’s minimum effort law or to a guidance law that enables mitigating saturation. The performance of the guidance law is analyzed theoretically and demonstrated on a few simulation cases.

Keywords: Guidance navigation and control, Mechatronic systems, Flying robots
Abstract: By appropriately modifying the generalized explicit guidance and developing it further, an optimal midcourse guidance law is proposed in this paper to ensure terminal impact angle constrained engagement of missiles with near-zero lateral acceleration in the terminal phase. This guidance law generates a desired intermediate point in space as well as the desired angles which needs to be achieved at the end of the midcourse. By reaching this intermediate point with the desired intermediate angles, it ensures that the missile will intercept the target with very less (near zero) acceleration demand in terminal phase with desired impact angles. When both the terminal impact angles are specified, the guidance law solves the constraint equations and nd out the intermediate point along with the angles, whereas if only one terminal angle is specified (or both are left free), it generates the intermediate point with missing angle (both angles) which can lead to minimum control eort and then solves that problem.

Keywords: Guidance navigation and control
Abstract: The observability of radome aberration estimation with line-of-sight (LOS) angle-only measurement is studied in this paper. When missile seeker can only measure LOS angle of target, target state variables are required to be estimated with this measurement for accurate missile guidance. Since LOS angle measurement contains error, caused by radome attached at missile nose and called radome aberration angle, the state estimation performance is degenerated. Thus, the radome aberration effects should be estimated and compensated for accurate target state variable estimation. The radome aberration angle is modeled as the product of radome slope and gimbal angle. The observability of state variables and radome slope estimation is analyzed for the relative dynamics with stationary target model in polar coordinate system. Simulations are conducted to verify the observability conditions obtained in this paper.