Keywords: Control over networks
Abstract: Design of modern control systems involves the analysis and synthesis of feedback controllers at multiple levels of abstraction, from fast feedback loops around actuators and subsystems, to higher level decision-making logic in supervisory controllers and autonomous systems. One of the major challenges in design of complex networked control systems -- such as those arising in aerospace, computing, robotics, critical infrastructure and manufacturing systems, to name a few -- is insuring that the combination of dynamical behavior and logical decision-making satisfy detailed safety and performance specifications. In many of these areas, verification and validation are now dominant drivers of schedule and cost, and the tools available for design of such systems are falling behind the needs of systems and control engineers, particularly in the area of systematic design of the mixed continuous and discrete control laws for networked systems.

This talk focuses on work over the last 10 years by a variety of groups interested in rigorous specification and systematic synthesis of decision-making logic for hybrid systems. This decision-making logic is responsible for selecting modes of operation for the underlying (continuous) control system, reacting to external events and failures in the system, and insuring that the overall control system is satisfying safety and performance specifications. Tools from computer science, such as model-checking and logic synthesis, provide new approaches to solving these problems. A major shift is the move from "design then verify" to "specify then synthesize" approaches to controller design that allow simultaneous synthesis of high-performance, robust control laws and correct-by-construction decision-making logic. This talk will provide an overview of the relevant theory and recent results in this area, as well as include examples of their application to autonomous vehicles and aircraft electrical power distribution systems.

Biography: Richard M. Murray received the B.S. degree in Electrical Engineering from California Institute of Technology in 1985 and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Sciences from the University of California, Berkeley, in 1988 and 1991, respectively. He is currently the Thomas E. and Doris Everhart Professor of Control & Dynamical Systems and Bioengineering at Caltech. Murray's research is in the application of feedback and control to networked systems, with applications in autonomy and synthetic biology. Current projects include specification, verification and synthesis of networked control systems; analysis and design biomolecular feedback circuits; and novel architectures for control using slow computing.

Keywords: Real time simulation and dispatching, Smart grids, Modeling and simulation of power systems
Abstract: Serial Windows-based programs are widely used in power utilities. For applications that require high-volume simulations, the sequential runtime can be in the order of days or weeks. The lengthy runtime, along with the availability of low-cost hardware, is leading utilities to seriously consider high performance computing (HPC) techniques. However, the majority of the HPC computers are Linux-based, and many HPC applications have been custom developed without considering existing simulation tools and ease of use. This has created a technical gap for applying HPC-based tools to today’s power grid studies using Windows-based tools. To fill this gap and accelerate the acceptance and adoption of HPC for power grid applications, this paper presents a prototype of a generic HPC platform for running Windows-based power grid tools on the Linux-based HPC environment. The preliminary results show that the runtime can be significantly reduced from weeks to hours to improve work efficiency.

Keywords: Optimal operation and control of power systems
Abstract: This paper proposes an approach to optimize the sizing and allocation of a fixed capacitor in a radial distribution network to compensate reactive power. The optimization problem is formulated as a minimization of the line loss of the network with the load profile within 24 hours. Constraints refer to node voltage quality and power flow. The approach is tested on the IEEE 33 nodes radial distribution network and the process of the optimization is analyzed from four aspects which are compensation nodes, power factor, active power load factor and compensation proportion to illustrate its feasibility and affectivity.

Keywords: Smart grids, Optimal operation and control of power systems, Control of renewable energy resources
Abstract: The intrinsic variability in non-dispatchable power generation raises important challenges to the integration of renewable energy sources into the electricity grid. This paper studies the problem of optimizing energy bids for a photovoltaic (PV) power producer taking part into a competitive electricity market characterized by financial penalties for generation shortfall and surplus. To this purpose, an optimization procedure is devised to cope with the intermittent nature of PV generation and maximize the expected profit of the producer. Since the optimal offer turns out to be a suitable percentile of the PV power cumulative distribution function (cdf), we investigate two approaches to properly take into account the effects of seasonal variation and non stationary nature of PV power generation in the estimation of PV power statistics. The first one normalizes the generated power with the power obtainable under clear-sky conditions. The second approach estimates a time-varying PV power cdf using only power data in a moving window of suitable width. A numerical comparison of the different bidding strategies is performed on a real data set from an Italian PV plant.

Keywords: Modeling and simulation of power systems, Intelligent control of power systems, Optimal operation and control of power systems
Abstract: This paper examines how custom computing hardware for fast power system analysis may be adapted to improve the user experience as well as its commercial-viability. One prime example of such custom hardware is the FPAA-based analog emulator developed previously by the authors. The authors propose development of a USB-based actuation and data acquisition interface that allows analog computational tools to work seamlessly with one or more commercially-available power system analysis software packages. A commercially-available software application will be tasked with user interaction and post-processing, while custom hardware is tasked with faster-than real-time power system analysis.

Keywords: Modeling and simulation of power systems, Nonlinear model reduction
Abstract: This paper proposes a means to quantify the aptitude of a dynamic model to be partitioned into weakly coupled submodels. The proposal applies to both linear and nonlinear systems, is largely independent of their scale, and requires information that is easy to provide on the part of the analyst. The presented indices can be exploited in different manners, from easing numerical integration on monolithic solution platforms to tailoring distributed or co-simulation ones based on the particular characteristics of the model at hand. Examples are reported to show the usefulness and the practical viability of the proposal.

Keywords: Intelligent control of power systems, Control system design, Modeling and simulation of power systems
Abstract: Power balancing of internal combustion engines is studied in time and frequency domains. Speed measurements at the flywheel and the load side are used to estimated the torque applied to the flywheel. The power contributions during the cylinder-wise work phases are estimated by integrating the reconstructed torque, and a controller which adjusts the fuel injections to reduce power unbalances is applied. A convergent power balancing algorithm is presented for multi-cylinder engines, where the work phases of the individual cylinders may overlap. This time-domain method is verified against previous frequency-domain approaches. The analysis shows that for engines with flexible crankshafts, the time-domain method performs in general differently from frequency-domain cylinder balancing approaches. The feasibility of the power balancing algorithm is investigated by simulations and full-scale tests on a large diesel power plant engine.

Keywords: Control of interconnected systems, Observers for linear systems, Robust linear matrix inequalities
Abstract: This paper proposes functional observer based controller for each subsystem of two interconnected systems. The new approach that we use to express the control error permits to use some results on large scale delay systems to solve the problem. The proposed controllers are free of the interconnection terms, and the observation errors are unbiased. The observers based controllers are designed via some matrices gains which are obtained via LMIs (i.e. via a tractable approach) from the Lyapunov stability results. An algorithm is given for the computation of the solutions. The result can be extended for the H_infinity control of linear and nonlinear large scale systems.

Keywords: Robust control, Adaptive control, Stability of nonlinear systems
Abstract: In this paper, we consider a class of second-order uncertain nonlinear systems. Besides parametric uncertainties, a non-parametric uncertainty may exist in every state equation or channel with its effects bounded by a known function. Such a bounding function is allowed to depend on all system states which means that the actual system does not need to meet the triangular structure. Therefore the currently available backstepping technique cannot be used to design controllers. To overcome such difficulty, a new backatepping-based robust adaptive control scheme is proposed. It is shown that the proposed scheme can ensure all signals in the closed-loop system bounded.

Keywords: Constrained control, Randomized algorithms, Convex optimization
Abstract: Robust Model Predictive Control (MPC) is a powerful technique to deal with constrained control of systems subject to bounded uncertainty. In the literature, many approaches have been proposed to find computationally feasible solutions. In this paper, we focus on robust MPC for linear systems affected by additive disturbances. Our aim is that of presenting a thorough comparison between two types of computational approaches: those that provide a solution that is robustly guaranteed, and randomized-based methods, which provide a solution with probabilistic guarantees only. The main outcome of the analysis is that the first type of approaches may show some conservatism in that they require the constraints to be loose in order to be feasible, while, although robustness cannot be a-priori guaranteed, randomized methods may lead to an effective solution - whose robustness can be experimentally verified - even for tight constraints. This enhances the use of randomized-based methods as a valid alternative to other approaches to robust MPC.

Keywords: Sliding mode control, Adaptive control
Abstract: This paper proposes an adaptive continuous higher order sliding mode control scheme. The control law comprises a structure which provides smooth finite time stabilization of the origin for a nominal disturbance-free system formed from a chain of integrators, together with a super-twisting term to guarantee robustness to a class of twice differentiable uncertainty. The super-twisting scheme is adaptive in nature and seeks to minimize the size of the gains whilst still ensuring sliding. The adaptive scheme has a novel dual-layer structure based on equivalent injection concepts, which necessitates that the first and second derivatives exist and are bounded, but knowledge of these bounds is not required. Simulation examples are provided to show the effectiveness of the proposed scheme.

Keywords: Sliding mode control, Adaptive control, Lyapunov methods
Abstract: An adaptive output feedback is presented. The adaptive gain is used to relax the restriction of knowing the perturbation bound. it is proved that with the adaptive gain both, the controller and the observer converge to the origin. A Lyapunov approach is used to proved the results and give restrictions on the xed gains of the controller and the observer.

Keywords: Application of nonlinear analysis and design, Process control, Real-time control
Abstract: Neural network with a specific restricted connectivity structure is used to identify a model of a real-life process. Parameters of the identified model are used to design a controller based on dynamic feedback linearization. The designed neural network based controller is verified on mathematical model within MATLAB/Simulink environment and applied to the real-time control of a plant. The static error is eliminated retuning input signal in the steady-state mode. Liquid level tank system was chosen as a case study to illustrate the applicability of the proposed approach. Experimental results have shown a good performance of the proposed technique. The designed controller is capable of tracking the desired water level for all set points with high degree of accuracy and without significant over/undershoot.

Keywords: Identification for control, Hybrid and distributed system identification, Hybrid and switched systems modeling
Abstract: Motivated by the study of complex motor control systems, we consider the identification and control of PieceWise Affine (PWA) systems and propose a novel data-driven framework that adaptively inverts the dynamics of such systems using noisy sampled data. First, we propose a novel PWA identification algorithm based on convex optimization applicable to both state--space and input/output models. Instead of considering the non-convex problem of searching over the entire parameter space of all submodels while assigning data to submodels, we propose to estimate a set of candidate submodels first and then select the best few submodels that best explain the given observations. Unlike the state-of-the-art clustering-based PWA identification algorithms, our method is robust to outliers, i.e., outlying submodel estimates will not affect the result of our proposed scheme. Given a PWA model of the dynamics obtained from the identification algorithm, we consider the control of the resulting hybrid system where our goal is to find an input that reproduces a given reference trajectory or that extremizes a performance criterion. To overcome computational limitations of state-of-the-art solutions to control of PWA systems, we cast the two problems of trajectory tracking and performance extremization in an optimization framework, and seek a scalable numerical algorithm. We demonstrate the effectiveness of our proposed framework on a model of a jumping robot.

Keywords: Identification for control
Abstract: In this paper the modeling of buildings thermal behavior is studied. The main goal is to develop a modeling procedure that can be used at different scales (a thermal zone, a floor or a whole building) and on different buildings. The scalability of the chosen black-box model structure is first assessed; simulation experiments are then conducted in order to test if the modeling procedure is reusable. As these tests are hardly feasible in practice, a real university building is first modeled using an energy simulation software. This model is then used to validate the proposed approach.

Keywords: Identification for control, Error quantification, Nonparametric methods
Abstract: Accurate uncertainty modeling is of key importance in high performance robust control design. The aim of this paper is to develop a new uncertainty modeling procedure that enhances the accuracy of the H-infinity norm. A frequency response based approach is adopted. The key novelty of this paper is a new method to address the intergrid error using local parametric modeling methods. These local polynomial and rational models enhance the estimates at the discrete frequency grid. Moreover, the presented methods are shown to enhance the intergrid error estimate. This is illustrated using simulations and experiments on an industrial active vibration isolation system. Compared to the local polynomial models, local rational models are able to handle lightly-damped resonances using far fewer data points.

Keywords: Identification for control, Nonlinear system identification
Abstract: In heating ventilating and air conditioning (HVAC) systems, typically two variables (air temperature and air humidity) have to be controlled via several (at least two) actuators. Some of the components show nonlinear behaviour. Therefore, HVAC systems belong to the class of nonlinear multi-input multi-output systems. A well suited approach to control this class of systems is model predictive control, since the time constants of HVAC systems are high (typically in the range of tens or hundreds of seconds) oering enough time to perform the required online optimization. In order to apply linear predictive control methods, while taking into account the nonlinearities of the plant, a modeling concept based on a physical plant model and a neuro-fuzzy model is proposed. The neuro-fuzzy model is obtained via the so called local linear model tree (LoLiMoT) algorithm. The generation of a linear state space representation from the neuro-fuzzy model is demonstrated. This linear state space model can then be used in a predicitive control scheme, where the linear model is updated each sampling instant from the neuro-fuzzy model. This technique allows the application of standard linear predictive control while taking into account the nonlinearities of the plant. Simulation and measurement results obtained from an industrial test plant are presented.

Keywords: Identification for control, Nonlinear system identification, Nonlinear adaptive control
Abstract: We consider the problem of adaptive observer design in the settings when the system is allowed to be nonlinear in the parameters, and furthermore they are to satisfy additional feasibility constraints. A solution to the problem is proposed that is based on the idea of employing observers comprising of an exponentially converging part coupled with exploratory dynamics (cf. Tyukin et al. (2013)). The procedure is illustrated with an example.

Keywords: Identification for control
Abstract: In this paper, we investigate an ensemble of atmosphere--ocean general circulation models (AOGCMs) participating in phase 3 of the Coupled Model Intercomparison Project (CMIP3), and whose dynamic behavior is emulated by the reduced-complexity climate model MAGICC6. Using superimposed impulses in solar radiative forcing for the purposes of system identification, we identify 12 AOGCMs in the CMIP3 ensemble for which low-order linear, time-invariant (LTI) models are able to very closely approximate MAGICC6 surface temperature projections under each of the four greenhouse gas (GHG) emission scenarios known as the Representative Concentration Pathways (RCPs) in the IPCC Fifth Assessment Report (AR5), even when extended to centennial timescales. The linear climate models identified in this paper are suitable for the analysis of feedback-based approaches to mitigation aimed at stabilizing global-mean surface temperature, and will inform future quantitative assessment of closed-loop approaches to geoengineering of the climate based on solar radiation management (SRM).

Keywords: Control over networks, Control under communication constraints, Control and estimation with data loss
Abstract: We consider the remote control of a noisy linear time-invariant (LTI) plant over erasure channels. In such a setup, it is well-known that when the controller-to-plant link provides perfect packet acknowledgements, the cost function is quadratic, and Gaussianity assumptions are in place, the separation principle holds and optimal controllers are linear. However, the corresponding optimal control law is computationally expensive and its steady-state performance is difficult to characterize. In this paper, we consider a class of controllers where all processing is affine, except for some elementary use of sensor-to-controller channel state information. For such controller class, we show that optimal designs also separate into an estimation and a state feedback design problem. Interestingly, our results show that the affine part of the controller converges, as the horizon length tends to infinity, to an LTI filter under the same conditions which guarantee mean-square stability in the well-known LQG control problem over erasure channels described above. Our infinite horizon proposal is computationally inexpensive and its steady-state behavior can be characterized straightforwardly.

Keywords: Control under computation constraints, Control under communication constraints
Abstract: In this paper we propose a novel quadratic model predictive control technique that constrains the number of active inputs at each control horizon instant. This problem is known as sparse control. We use an iterative convex optimization procedure to solve the corresponding optimization problem subject to sparsity constraints defined by means of the ell_0-norm. We also derive a sufficient condition on the minimum number of active of inputs that guarantees the exponential stability of the closed-loop system. A simulation example illustrates the benefits of the control design method proposed in the paper.

Keywords: Control of networks, Control over networks, Complex system management
Abstract: The studies on the controllability of complex networks popularly existing in natural, social and man-made engineered systems have been a critical and attractive subject for both academic and practical communities. To design and maintain a networked system under control, it is vital to explore the mechanism and relationship between the network layout and its controllability. For a fully-controlled complex system, potential malicious attacks and/or random failures will lead to the damage of its internal structure, such as the breakdown of certain control nodes or the loss of the links between state nodes. In this paper, we first introduce the concept of degree of controllability to quantify the control level of the networks. And for the networks whose degree of controllability is not full, we propose two novel optimal recovering strategies, OAN (short for optimal adding-node) strategy and OAE (short for optimal adding-edge) strategy, to repair their controllability. The results of experiments conducted on the various real and model networks demonstrate the effectiveness of these two strategies and the better performance compared to their randomized counterparts, RAN (short for randomized adding-node) strategy and RAE (short for randomized adding-edge) strategy.

Keywords: Networked embedded control systems, Nonparametric methods, Switching control
Abstract: A performance analysis of automotive semi-active suspension control algorithms was realized. Three on/off control strategies were tested in a quarter of vehicle model: hybrid Sky Hook-Ground Hook, hybrid Mix-1-Sensor and Frequency Estimation-Based controller. A commercial Magneto-Rheological damper was modeled by using an Artificial Neural Network approach. The automotive semi-active suspension was implemented in a commercial Controller Area Network system; and the control algorithms were implemented in a micro-controller system with comfort and road holding as main goals. Early results show the feasibility of this application.

Keywords: Control under communication constraints, Control over networks
Abstract: Design of optimal controllers for uncertain linear systems subject to communication constraints is investigated in this paper. By following the Model-Based Event-Triggered (MB-ET) control framework we are able to design optimal control laws using the nominal models that are robust to model uncertainties and limited communication. For finite horizon optimal control problems we also provide sub-optimal schedulers that estimate the best time instants to send feedback measurements. The nominal optimal controller and sub-optimal scheduler aim at optimizing the performance of the system that considers the control effort as well as the cost of communication both in the presence of model uncertainties.

Keywords: Sensor networks, Statistical data analysis, Networked robotic systems
Abstract: The diffusion of visual sensor networks, and in particular of smart camera networks, is motivating an increasing interest on the research of distributed solutions for several vision problems. Specifically, in this paper we propose a distributed solution to the problem of reconstructing target positions in large Motion Capture (MoCap) systems. Real time reconstruction by means of centralized procedures is practically unfeasible for very large systems, while the use of distributed computation allows to significantly reduce the computational time required for reconstruction, thus allowing the development of real time solutions.

Then the proposed distributed reconstruction procedure is optimized by exploiting information about the structure of the system: the visibility matrix states which objects in the scene are somehow measurable by a sensor (sensor-object matrix). Often, the typical localization of data from real application scenarios induces an underlying structure on the visibility matrix, that can be exploited to improve the performance of the system in understanding the surrounding environment. Unfortunately, usually these data are not properly organized in the visibility matrix: for instance, listing the sensors in a pseudo-random order can hide the underlying structure of the matrix. This paper considers the problem of recovering such underlying structure directly from the visibility matrix and designs an algorithm to perform this task.

Our simulations show that the distributed reconstruction algorithm optimized by means of the estimation of the structure of the visibility matrix achieves an important computational time reduction with respect to the standard (centralized) reconstruction algorithm.

Keywords: Identification and control methods, Application of mechatronic principles, Mechatronic systems
Abstract: In this study, the implementation of the sliding mode control method to fin loading systems which are utilized in performance tests of control actuation systems converting the flight commands into physical motion in autonomous aerial systems is investigated. For this purpose, the mathematical model of a hydraulically-actuated fin loading system designed to accomplish the mentioned objective is built and then the sliding mode control schemes involving both constant and varying sliding surfaces are constructed. Having completed the computer simulations upon these models under specified conditions, real-time performance tests are conducted using the experimental setup developed. As a result of the simulations and experiments, it is observed that the varying-surface sliding mode control approach exceeds other control methods examined in performance.

Keywords: Identification and control methods, Motion Control Systems, Modeling
Abstract: This paper considers a micrometric positioning system based on a dielectric electro-active polymer membrane. The motion is generated by the deformation of the membrane caused by the electrostatic compressive force between two compliant electrodes applied on the surface of the polymer. The paper suggests various model-based design strategies (in both time and frequency domains) for PID control laws, which are able to compensate the nonlinear behavior of the actuator (caused by the characteristics of the material and the annular geometry of the membrane) and obtain very precise tracking of steps or sinusoidal reference signals. The various design strategies are discussed and compared both in simulations and experiments.

Keywords: Mechatronic systems, Robotics technology, Telerobotics
Abstract: This paper presents a performance metric for the manipulability of constrained serial manipulators. The manipulability measure and manipulability ellipsoid are found from the manipulability Jacobian, which is easily found for serial manipulators as the standard Jacobian matrix, or for parallel manipulators as the Jacobian that gives the mapping from the active joint velocities to the end-effector velocities. For serial manipulators with constraints on the kinematic chain, however, this measure is not straightforwardly found because the manipulability Jacobian cannot be found in the same way as for the cases above. In this paper we propose to use the Constrained Jacobian Matrix, i.e., an analytical mapping between the end-effector and joint velocities that also takes the chain constraints into account. This analytical representation of the Constrained Jacobian Matrix allows us to find a performance metric describing the manipulability of a serial manipulator with chain constraints in the same way as for non-constrained kinematic chains and parallel manipulators. The approach allows us to compare the manipulability of the end effector of non-constrained serial manipulators and constrained serial manipulators and we study the important case of robotics-assisted minimally invasive surgery where the arm is constrained at the entry point.

Keywords: Mechatronic systems, Modeling
Abstract: This paper presents two nonlinear model-based control designs for a hydraulic system that consists of two mechanically coupled hydraulic cylinders actuated each by a separate servo-valve. Based on a physically-oriented nonlinear mathematical model of the test rig, a further model simplification results in a completely controllable MIMO system. Two different control structures are discussed and compared to each other in this paper: First, a cascaded flatness-based control is designed, where fast inner control loops determine the difference pressure in each hydraulic cylinder, while the position as well as the generated force is controlled in the outer loop. In the second approach, a centralised flatness-based control, with the same outputs as in the outer loop of the cascaded approach, is developed for the MIMO system. Model parameter uncertainties are estimated by a reduced-order disturbance observer and compensated by the control algorithm. The efficiency of the proposed control structures is demonstrated by experimental results from a dedicated test rig.

Keywords: Mechatronic systems, Motion Control Systems
Abstract: This work discusses several approaches to the multi input, single output problem in hydraulic control. The multi input problem arises from the fact, that the actuator force in a hydraulic cylinder is the result of a combination of two pressure states, which is not uniquely defined. With each individual pressure state there may be associated a subsystem with a control input. To achieve a desired actuator force there are inifinitely many combinations of pressure values possible, and it is therefore unclear how to chose the tracking reference values for the pressure subsystems. Secondary objectives are defined, with time optimality of the step response of the subsystems being one of them, that can be used to solve the multi input problem by treating it as an optimization problem. The practical implications of the optimal solutions are discussed and a new solution is proposed, motivated by the results of the optimization problems. The proposed solution is used to develop a backstepping control for a hydraulic multi input single output system, and its performance is shown in simulation.

Keywords: Identification and control methods, Smart Structures, Smart Sensors and Actuators
Abstract: In the literature, the generalized Bouc-Wen model can track precisely asymmetric hysteresis nonlinearity. In this paper, we propose to extend this generalized model to multivariable hysteresis model that can track the nonlinearities in multi-degrees of freedom (multi-DoF) hysteretic actuated systems. In particular, these systems are typified by strong hysteresis couplings. Then, a method of identication of the multivariable hysteresis model is proposed. Finally, based on the inverse multiplicative structure, we propose a multivariable feedforward compensator of the nonlinearity. The proposed approach has been applied to a multi- DoF piezoelectric tube (piezotube) used in scanning probe microscopy and the experimental verification demonstrated its validity in terms of model precision and compensation efficiency.

Keywords: Decision support and control, Physiological Model, Identification and validation
Abstract: Hyperglycemia is common in patients hospitalized for critical illness, trauma or after surgery, and is associated with increased morbidity and mortality. The Glucosafe system was developed to provide decision support for control of stress hyperglycemia in the intensive care unit. Glucosafe uses an insulin-glucose model to predict blood glucose (BG) concentrations, based on the patients previous and current insulin infusion, nutrition, and BG measurements. As endogenous insulin production is dependent on BG in a negative feedback loop, a pancreas model of endogenous insulin production was incorporated into the Glucosafe system. We investigated the stability of the feedback loop by calculating the loop gain of the model at different steady- state BG concentrations and insulin sensitivities. We also examining the models BG oscillations, after an initial perturbation, at different insulin sensitivities. Results show that both steady-state BG and insulin sensitivity influences the size of the loop gain. The largest loop gain (6.9) occurs at an insulin sensitivity of 1.2 and a BG of 5.5 mmol/l, where the BG perturbation resulted is damped oscillations of BG and endogenous insulin production. The BG dependent endogenous insulin production did not result in an unstable Glucosafe insulin-glucose model, although it did introduce damped oscillations in BG and insulin production during rapid increases in BG. It may be prudent to investigate if these oscillations can be decreased, possibly via the construction of a pancreas model which includes both a phase-1 and phase-2 response.

Keywords: Physiological Model, Artificial pancreas or organs
Abstract: Hyperglycaemia is prevalent in critical illness and clinical practice is to reduce blood glucose by intravenous insulin infusion. We estimated pancreatic secretion, hepatic first-pass extraction ratio and plasma insulin clearance per minute from measured serum C-peptide and insulin in 9 critically ill adults in a pseudo steady-state situation, i.e. when nutrition and insulin infusion remained at a constant rate up to the test (min 20, median 240, max 510 min). To estimate pancreatic secretion, a population C-peptide kinetics model was used to convert C-peptide concentration to C-peptide secretion rate. Pancreatic secretion varied 18-fold from 5.2 to 93.5 mU·min-1, and the lowest secretion rates were in patients older than 70 years or patients with type 2 diabetes. Pancreatic secretion correlated positively, but not significantly with blood glucose. Blood glucose was not correlated with plasma insulin. A two-dimensional regression analysis of hepatic first-pass extraction and plasma insulin clearance showed that the smallest relative error between estimated plasma insulin and measured plasma insulin was obtained for an extraction ratio of 72% and plasma clearance of 0.34 min-1. Using these values, a negative correlation was found between post-hepatic insulin production and the rate of insulin infusion. These results indicate that 1) hepatic insulin extraction is increased in critical illness. This is also confirmed by the observation that steady-state plasma insulin concentrations in this study were relatively lower when compared to steady-state measurements in normal subjects; 2) blood glucose drives pancreatic secretion moderately; 3) there is substantial variation in pancreatic secretion between patients that cannot be explained from the blood glucose variation, but could be related to patient age and diabetic state; 4) there was also substantial variation in insulin sensitivity between patients, since similar levels of insulin sensitivity would have predicted a negative correlation between blood glucose and plasma insulin; 5) insulin infusions are used to compensate for inadequate pancreatic insulin secretion.

Keywords: Control of physiological and clinical variables, Clinical trial, Intensive and chronic care or treatment
Abstract: Glycaemic control (GC) in the intensive care unit (ICU) has proven difficult and contentious. Continuous glucose monitors (CGM) have been mooted as a solution to provide better control with less clinical effort. The aim of this study was to assess the reliability of CGM devices in critically ill patients, as well as the impact of device type (inter-device) and sensor location (inter-site) on performance. Ten patients were enrolled in this pilot trial and each patient was monitored using 3 concurrent CGM devices: a Medtronic Guardian real-time on their abdomen and Medtronic iPro2 recorders on their abdomen and thigh. The Guardian real-time had an overall MARD of 24%, compared to ~12% for the iPro2 devices. Bland Altman plots showed Guardian SG errors were associated with BG level, but iPro2 SG errors were not associated with BG level. Inter-device SG discrepancies were larger than inter-site discrepancies, when comparing concurrent data and CGM device type, or calibration method, tends to have a larger impact on SG accuracy than sensor location. Three case studies showed several interesting findings regarding CGM behaviour in critically ill patients. CGM devices are capable of performing very well in critically ill patients, but certain illnesses/conditions, as well as drugs/therapies may impact SG data. These factors require further investigation before CGMs can be implemented as standard clinical practice and/or for GC.

Keywords: Identification and validation, Chronic care and/or diabetes, Decision support and control
Abstract: Virtual trials have proved useful in developing safe and efficacious glycaemic control protocols. However, these trials rely on lumping all changes in patient condition into the insulin sensitivity parameter. As electronic data collection provides higher temporal resolution than paper-based charts, irregular timings of both therapies and measurements clash with a regular, hourly insulin sensitivity profile. Additionally, unobservable endogenous changes are a factor for hour-to-hour variability. This research extends the virtual trial protocol to natively handle irregular data by regularising the insulin sensitivity profile, and utilising a simple stochastic differential equation. The insulin sensitivity profile was re-interpreted as a b-spline basis, allowing a higher order description with greater local support. The fitting error resulting from this regularisation was absorbed by a stochastic element in the glucose compartment, representing the hour-to-hour changes that cannot be attributed to changes in insulin sensitivity. The resulting virtual patients were demonstrated to be equivalent to the originals when a 0th order basis was used. Inclusion of the stochastic element in this case simply ensured the model still fitted during periods of unmodelled high endogenous glucose production, while a 2nd order basis uses this element to natively control the balance between changes in patient state and hour-to-hour unmodelled changes due to noise and endogenous processes. The resulting virtual trials are thus better able to preserve information in irregular data sets, and regulate the balance between controllable and uncontrollable glycaemic changes.

Keywords: Healthcare management, disease control, critical care, Decision support and control
Abstract: Stress-induced hyperglycaemia and glycaemic variability are both associated with worsened critically ill patient outcome. Glycaemic control (GC) aims to reduce and stabilize blood glucose (BG) levels, while minimizing hypoglycaemic risk. Several national surveys have been carried out about GC in intensive care unit (ICU). They mainly focused on characteristics of a GC protocol in terms of glycaemic target, on hyper/hypo-glycaemic thresholds and on the association between glycaemic levels and patient outcome. This survey aims to have a more overall European overview about GC, and more especially about the interest of medical staff for GC systems in European ICUs and the clinician specified key success factors related for these systems. This survey was conducted with 52 persons related to ICUs from 18 different European countries. Results show that some ICU clinicians (10/52, 19.2 %) are still reluctant to implement GC and attention should be paid to overcome their specific criticisms to aid the uptake of safe, effective GC systems. In particular, control-related issues of GC target and used centred on fear of hypoglycaemia. Clinicians thus preferred model-based systems (19/52, 36.5 %), especially if they could forecast the risk of hypoglycaemia. This survey results have highlighted the definition of key control issues by the end-user.

Keywords: Kinetic modeling and control of biological systems, Decision support and control, Healthcare management, disease control, critical care
Abstract: Critically ill patients suffer from “stress hyperglycemia,” a diabetes-like condition of elevated glucose concentrations. The outcomes from controlling glucose levels are mixed; some trials have shown significant reductions in morbidity and mortality rates for patients, while the NICE-SUGAR trial debates this result. The current state of critical care practice is a conservative approach to glucose control, where physicians maintain glucose levels via strategic administration of insulin, and the result is mild-to-moderate hyperglycemia for patients. Automating this insulin delivery process can improve glucose control, while mitigating hypoglycemia, using mathematical model-based tools. Key to clinical implementability and performance is a subcutaneous insulin delivery model. The proposed model is a reduction of an extended Wilinska model (Wilinska et al. (2005)) that captures plasma insulin dynamics after insulin administration. The proposed model holds for regular and rapid-acting insulins administered via bolus injection or continuous infusion. The model parameters were fit to clinical data from insulin-dependent diabetics and healthy patients administered insulin. Regular insulin data were fit simultaneously across three dose levels by adjusting the rate parameters. Fast acting insulin data were simultaneously fit separately from regular insulin, and similarities and differences between fast-acting insulin analogues were observed. The subcutaneous model, integrated with our previously-published whole body model (Roy and Parker (2006)) is able to accurately capture plasma glucose levels of patients published in the literature. Our overall objective is to couple this system-level model to a model-based control algorithm to facilitate clinical decision-making for glucose control and insulin delivery in critical care.

Keywords: Systems with time-delays, Robust linear matrix inequalities
Abstract: This paper focuses on delay-dependent bounded input bounded output (BIBO) stabilization of a class of two-dimensional (2D) discrete delayed systems described by Fornasini and Marchesini (FM) state-space models. The main contribution is to propose a linear matrix inequality (LMI) method to construct sufficient conditions on delay-range- dependent 2D BIBO stability, where the stability criterion guarantees that bounded input can lead to bounded output though the time delay has a direct effect on the system stability. To solve the problem, a novel relevant concept of 2D BIBO stability is first introduced. Then, the condition is formulated in terms of LMIS through the Lyapunov–Krasovskii function. Subsequently, the existence condition can be extended to the uncertain case. Meanwhile, the relation between BIBO stability and asymptotical stability is expatiated. The generality of the proposed design method is shown by results through constant delay case. Finally, an illustrative example of a causal 2D system is selected to demonstrate the effectiveness and merits of the proposed method.

Keywords: Systems with time-delays, Systems with saturation, Time-varying systems
Abstract: In this paper the problem of local stabilization of nonlinear discrete-time systems with time-varying delay and saturating actuators is studied. Firstly, through a fuzzy Lyapunov-Krasovskii (L-K) function, we develop convex conditions to synthesize fuzzy state feedback gain controllers that stabilize the nonlinear system subject to saturating actuators. Next, we introduce a new approach to compute an estimate of the region of attraction where the initial condition sequence is split into two subsequences. The first one is composed of the state vector at the actual instant of sampling, i.e. for k=0. The second one is composed of the state vectors at the delayed samplings. Then, we propose a convex optimization problem to maximize the estimated region of attraction of the closed loop control system. Finally, we give a numerical example to illustrate the obtained results.

Keywords: Systems with time-delays, Systems with saturation, Stability of nonlinear systems
Abstract: Basins of attraction are instrumental to study the effect of input saturation in control systems, as these sets characterise the initial conditions for which the control strategy induces attraction to the desired equilibrium. In this paper, we describe these sets when the open-loop system is exponentially unstable and the system is controlled by a single actuator with both constant time-delays and saturation. Estimates of the basin of attraction are provided and the allowable time-delay in the control loop is determined with a novel piecewise quadratic Lyapunov-Krasovskii functional that exploits the piecewise affine nature of the system. As this approach leads to sufficient, but not to necessary conditions for attractivity, we present simulations of an exemplary system to show the applicability of the results.

Keywords: Time-invariant systems, Infinite-dimensional systems, Systems with time-delays
Abstract: In this paper some results about constrained Input-Output systems are presented. The considered framework is the Callier-Desoer Class of convolution systems, which covers both finite dimensional systems and infinite dimensional ones such as systems with time delays. Based on bounds calculus for the input-output gain of the convolution kernel, and convex sets properties, a characterization of the output reachable set is first presented. Necessary and sufficient conditions are then derived to warrant the constraints meeting, when the system is subject to polyhedral constraints on both its inputs and outputs. A numerical example is given to illustrate the proposed approach on a time delayed system.

Keywords: Systems with time-delays, stability of delay systems, Lyapunov methods
Abstract: This paper deals with linear time-invariant systems with multiple delays. We present a necessary and sufficient stability condition, expressed in terms of the delay Lyapunov matrix. This result generalizes the well known Lyapunov theorem for delay free linear systems. An illustrative example shows how to apply the presented condition.

Keywords: Flying robots, Modeling, Motion Control Systems
Abstract: Through equipping manipulators on a mobile robot to actively influence its surroundings has been extensively used in many ground robots,underwater robots and space robots systems. However, the Rotor Flying Robot(RFR) is difficult to be made such re-design. This is mainly because 1)the equipped manipulator will bring heavy coupling between it and the RFR system, which make the whole system model highly complicated and make the central controller design is impossible.2)the movement of the manipulator will introduce a great deal time-varying disturbance to the RFR system, which make distributed control design is also challenging. Thus, in this paper, utilizing the Lagrange equation, the exact dynamics model of a combined system, called Rotor-Flying Multi-Joint Manipulator(RF-MJM), is constructed and its complicacy is analyzed and presented . Then, a linearized full-state feedback Linear Quadratic Regulator(LQR)controller is designed through obtaining trim point near hovering state. Finally, simulations using MATLAB are conducted and the results are analyzed to show the basic control performance of the LQR controller.

Keywords: Telerobotics, Mobile robots, Work in real and virtual environments
Abstract: This paper presents a novel obstacle avoidance approach that is capable of dealing with both static and dynamic obstacles in the environment with guaranteed collision-free navigation for haptic teleoperation of VTOL aerial robots. The proposed approach modulates the set point for the vehicle's controller based on the user input energy, estimated potential energy and vehicle's kinetic energy. By shuffling the potential and kinetic energy, vehicle's velocity is regulated according to the permissible kinetic energy and thus obstacle avoidance is achieved. With careful design of the potential field, this approach offers a guaranteed collision-free navigation with the presence of both stationary and moving obstacles. Incorporating the novel approach with the emph{Dynamic Kinesthetic Boundary}, the human operator can better perceive the environment where the robot is deployed through the rich spatial haptic cues rather than an onset gradual single force vector. Analysis is provided and proves that in the case of perfect velocity tracking of the slave system, the proposed algorithm can guarantee a collision-free navigation through the environment. Simulations and experiments were conducted, and the results provide verification of the effectiveness of the proposed approach in obstacle and collision avoidance for haptic teleoperation of aerial robots.

Keywords: Flying robots
Abstract: An innovative application field for Unmanned Aerial Systems (UASs) is the subject of this paper. The aerial robotic execution of technical activities based on autonomous aerial platforms poses important challenges, as realistic industrial activities are physically demanding. Typical environment-modifying tasks, such as surface grinding, require the exertion of significant forces in order to be successfully executed. For such purposes, the exploitation of thrust-vectoring actuation is proposed, and a methodology for achieving longitudinal force exertion while retaining safe operation is developed, relying on the platform's exceptional actuation features, a piecewise-affine representation of the system modes, and an explicit model predictive control scheme. The experimental demonstration of the proposed strategy is conducted utilizing a compound UAS, consisting of a high-end tilt-rotor vehicle, mounted with an end-effector which carries a motorized tool customized for surface grinding tasks.

Keywords: Flying robots, Mobile robots, Guidance navigation and control
Abstract: This paper proposes an adaptive sliding backstepping control law for quadcopter attitude control. By employing adaptive elements in the sliding mode control formulation the proposed control law avoids a priori knowledge of the upper bounds on the uncertainty. The proposed controller can be used for systems that are in strict feedback form with matched uncertainties. Numerical simulations show that this control method is capable of guaranteeing global asymptotic tracking of the desired attitude trajectory.

Keywords: Flying robots, Robots manipulators, Autonomous robotic systems
Abstract: This paper deals with the trajectory tracking control for quadrotor aerial vehicles equipped with a robotic manipulator. The proposed approach is based on a two-layer controller: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables while in the bottom layer, an adaptive motion control algorithm is in charge of tracking the motion references. A stability analysis of the closed-loop system is developed. Finally, a simulation case study is presented to prove the effectiveness of the approach.

Keywords: Guidance navigation and control, Flying robots, Modeling
Abstract: This paper presents recent advances in the project: development of a convertible unmanned aerial vehicle (UAV). This aircraft is able to change its flight configuration from hover to level flight and vice versa by means of a transition maneuver, while maintaining the aircraft in flight. For this purpose a nonlinear control strategy based on Lyapunov design is given. Numerical results are presented showing the effectiveness of the proposed approach.

Keywords: Model predictive and optimization-based control
Abstract: Robust model predictive control algorithms often suffer from a high computational complexity due to the large number of variables and constraints involved in the optimiztion problems that are solved at every sampling instant. In this paper we propose an approximate control scheme for polytopic systems based on the interpolation of offline computed robust invariant tubes. The feasible set of the control scheme is the convex hull of all the sets in the invariant tubes. Online, the current control input is computed by interpolating between the control laws associated with these tubes. This interpolation requires the solution of an optimization problem. Compared with a direct solution of a robust model predictive control problem, the interpolation approach proposed in this paper requires much less computation time.

Keywords: Real time optimization and control, Model predictive and optimization-based control, Nonlinear process control
Abstract: In this paper, an MPC that explicitly integrates the RTO structure into the dynamic control layer is presented. In particular, a robust MPC is proposed, which takes into account the uncertainties that arise from the difference between nonlinear and linear models, by means of a multi-model approach: a finite family of linear models is considered, which operate appropriately in a moderate-To-large region around a given operating point. In this way, each linear model provides an enough accurate description of the system. Feasibility and stability conditions are preserved. Moreover, the real plant converges to the optimal point that optimizes the economic cost function.

Keywords: Model predictive and optimization-based control, Nonlinear process control
Abstract: A nonlinear model predictive control scheme guaranteeing robust constraint satisfaction is presented. The scheme is applicable to polynomial or rational systems and guarantees that state, terminal, and output constraints are robustly satisfied despite uncertain and bounded disturbances, parameters, and state measurements or estimates. In addition, for a suitably chosen terminal set, feasibility of the underlying optimization problem at a time instance guarantees that the constraints are robustly satisfied for all future time instances. The proposed scheme utilizes a semi-infinite optimization problem reformulated as a bilevel optimization problem: The outer program determines an input minimizing a performance index for a nominal nonlinear system, while several inner programs certify robust constraint satisfaction. We use convex relaxations to deal with the nonlinear dynamics in the inner programs efficiently. A simulation example is presented to demonstrate the approach.

Keywords: Real time optimization and control, Control and optimization of supply chains, Batch and semi-batch process control
Abstract: The steady advance of computational methods makes model-based optimization an increasingly attractive method for process improvement. Unfortunately, the available models are often inaccurate, which results in significant plant-model mismatch. An iterative optimization method called "modifier adaptation'' overcomes this obstacle by incorporating plant information into the optimization framework. A particular situation is when the plant operates under feedback control and only a model of the open-loop system is available. This paper extends modifier adaptation for constrained optimization problems to that particular situation. The inputs of the controlled plant are the setpoints, whereas the model inputs are the manipulated variables. Using this open-loop model and process measurements, the proposed algorithm guarantees both optimality and constraint satisfaction for the controlled plant upon convergence. A simulated CSTR example with constraints illustrates the method.

Keywords: Model predictive and optimization-based control, Nonlinear process control, Advanced control technology
Abstract: A relatively recent approach for robust Nonlinear Model Predictive Control (NMPC) is based on scenario trees with a so-called recourse formulation. This approach is of interest, because it is less conservative than worst-case robustification approaches. A major challenge when using scenario trees for robust NMPC is the large number of scenarios, which grows exponentially. This exponential growth quickly becomes a bottleneck for the computational costs, which need to stay within bounds that permit real-time applicability. We present how to generate scenarios based on a quadrature rule for the expectation value of an arbitrary economic objective function. The use of sparse grids for the quadrature of the high-dimensional stochastic integrals yields a drastically smaller number of scenarios than the tensor grid approaches used so far. We compare the performance of several robust NMPC approaches for a distillation column with three normally distributed uncertain parameters within a simulated Monte-Carlo controller testbed.

Keywords: Model predictive and optimization-based control, Real time optimization and control, Nonlinear process control
Abstract: In this paper we present a systematic and efficient approach to deal with uncertainty in Nonlinear Model Predictive Control (NMPC). The main idea of the approach is to represent the NMPC setting as a real-time decision problem under uncertainty that is formulated as a multi-stage stochastic problem with recourse, based on a description of the uncertainty by a scenario tree. This formulation explicitly takes into account the fact that new information will be available in the future and thus reduces the conservativeness compared to open-loop worst-case approaches. We show that the proposed multi-stage NMPC formulation can deal with significant plant-model mismatch as it is usually encountered in the process industry and still satisfies tight constraints for the different values of the uncertain parameters, in contrast to standard NMPC. The use of an economic cost function leads to a superior performance compared to the standard tracking formulation. The potential of the approach is demonstrated for an industrial case study provided by BASF SE in the context of the European Project EMBOCON. The numerical solution of the resulting large optimization problems is implemented using the optimization framework CasADi.

Keywords: Switching stability and control, Control design for hybrid systems, Robust estimation
Abstract: The goal of this paper is to study the H2-control for Markov Jump Linear Systems(MJLS) assuming that the controller doesn’t have access to the Markov parameter but, instead, there is a detector that emits signals which provides information on this parameter. The idea is to use the information provided by this detector in order to design a feedback linear control that stochastically stabilizes the closed loop system. A Linear Matrix Inequalities (LMI) formulation is provided in order to achieve this goal. In the sequel we deal with the H2 control problem and we show that again an LMI optimization problem can be formulated in order to design a stochastically stabilizing feedback control with guaranteed H2-cost. We also present some conditions under which our results recast the usual results for the H2 control of MJLS as presented in Costa et al. (2004). The case with convex polytopic uncertainty on the parameters of the system and on the transition probability matrix is also considered.

Keywords: Decentralized control, Switching stability and control, Output feedback control (linear case)
Abstract: Large scale engineering systems are typically composed by several subsystems that interact with each other as a result of material, energy and information flows. A high performance control technology such as decentralized control can be employed for control this class of systems. An optimal decentralized control architecture is presented in this paper in order to ensure the efficient management of an inland navigation network in a global change context. For further logic, we show that if the subsystems present a connectivety from one to another, suitable local feedback action provided by these subsystem (decentralized controllers) may be sufficient if the subsystems are connected. Stabilizability (resp. stability) conditions are given for perfectly decoupled and interconnected coupled (by connectivity parameters) subsystems.

Keywords: Switching stability and control, Digital implementation, Lyapunov methods
Abstract: This paper addresses the control and scheduling design for control systems sharing a limited computation resource, especially embedded control systems (ECSs). The complementary design of controller and scheduler is for improving the control performance and achieving an ecient utilization of the limited computation resources. A novel PI control design method as well as a novel output-based scheduling approach are introduced to achieve setpoint-tracking for multiple plants. For verifying stability the whole system, namely all plants with the scheduled controllers, is modeled as a discrete-time switched linear system. The stability criteria is eventually formulated as a linear matrix inequality (LMI) feasibility problem. The eectivenes of the proposed control and scheduling approach is illustrated by a practical implementation where two DC motors are controlled by one processor.

Keywords: Fault-tolerant, Switching stability and control, Diagnosis
Abstract: This paper deals with the design of an estimator-based supervisory Fault Tolerant Control (FTC) scheme for Linear Time Invariant (LTI) systems. A formal stability proof based on dwell-time conditions is presented when the state of the detection filter does not correspond to the plant state, as in classical (Luenberger) observer-based approaches. In this context, fault isolability could be improved leading to an enhanced distinguishability between all possible operating modes. Note that this paper should be understood like a preliminary work to provide a unified context for a norm-based performance optimization problem. The efficiency of the proposed technique is illustrated on a numerical example.

Keywords: Switching stability and control, Robust linear matrix inequalities
Abstract: The problem of controller synthesis in a fixed time interval for discrete-time switching Markov jump systems is dealt with in this paper. Compared with the existing results, the new proposed stabilization conditions are obtained by permitting the stochastic Lyapunov energy function to rise at each switching instant, but the switching signal is constraint by an average dwell time. Finally, the validity of the obtained results is demonstrated with an example.

Keywords: Control system design, Application of power electronics
Abstract: This paper proposes an offset-free model predictive control (MPC) method for output voltage regulation of the three phase inverter for an uninterruptible power supply (UPS) application. A use of a disturbance observer (DOB) is made to estimate the unknown disturbance caused by the load current and a plant-model mismatch. The proposed MPC optimizes the one step ahead cost function. The online computation considering the input constraint of the inverter system is very simple. It is shown that the closed-loop system is globally asymptotically stable in the presence of input constraints and that the integral action in the DOB eliminates the steady state errors caused by a plant-model mismatch. The effectiveness of the closed-loop system is experimentally shown under a use of resistive load.

Keywords: Optimal operation and control of power systems, Real time simulation and dispatching, Control of renewable energy resources
Abstract: The photovoltaic-diesel-battery (PDB) hybrid system is proposed previously to satisfy power requirements in some remote areas locating out of national power grid. However, to dispatch the uses of different components in PDB hybrid system remains a problem. In this paper, mathematical model of the PDB hybrid system is transformed into an MIMO linear state-space form, and model predictive control (MPC) is applied to its energy dispatching problem. In the MPC design, an objective function is constructed to penalize the use of diesel generator and battery bank, while the use of photovoltaic array is encouraged. Constraints of the system are modeled according to its practical limitations, and are expressed in a compact form. The proposed MPC is designed by using the standard receding horizontal technique. Performances of the closed-loop system is demonstrated by simulation examples, where disturbances in load demand and photovoltaic (PV) power are considered.

Keywords: Optimal operation and control of power systems, Analysis and control in deregulated power systems, Smart grids
Abstract: High Voltage Direct Current (HVDC) lines allow large quantities of power to be transferred between two points in an electrical power system. A Multi-Terminal HVDC (MTDC) grid consists of a meshed network of HVDC lines, and this allows energy reserves to be shared between a number of AC areas in an efficient manner. Secondary Frequency Control (SFC) algorithms return the frequencies in areas connected by AC or DC lines to their original setpoints after Primary Frequency Controllers have been called following a contingency. Where multiple TSOs are responsible for different parts of a MTDC grid it may not be possible to implement SFC from a centralised location. Thus, in this paper a simple gain based distributed Model Predictive Control strategy is proposed for Secondary Frequency Control of MTDC grids which allows TSOs to cooperatively perform SFC without the need for centralised coordination.

Keywords: Stochastic optimal control problems, Control of constrained systems, Power systems
Abstract: In this paper we investigate the problem of optimal real-time power dispatch of an interconnection of conventional power generation plants, renewable resources and energy storage systems. The objective of the problem is to minimize imbalance costs and maximize the profit of the company managing the system whilst satisfying user demand. The managing company is able to trade energy on an electricity market. Energy prices on the market, user demand and intermittent generation from the renewable plants are considered stochastic processes. We show that under certain assumptions, the stochastic power dispatch problem over a finite horizon can be recast, under a proper choice for the feedback policies and for the disturbance set, into a stochastic optimization formulation but with deterministic constraints. We carry out a systematic study of stochastic optimization methods to solve this problem, in particular we analyze the stochastic gradient method. We also show that this problem can be approximated by a proper deterministic optimization problem using the sample average approximation method, which can then be solved by standard means.

Keywords: Control system design
Abstract: In this study, we investigate the maximum possible profit for a commercial office building participating in New York’s Day-Ahead Demand Response (DADR) program. We formulate an optimal control problem, assuming perfect knowledge of future weather, occupancy, and day-ahead electricity price predictions to examine this potential benefit. Then, a practical control strategy based upon the framework of Model Predictive Control (MPC) is proposed, which enables a building to participate in the DADR program. The controller decides once every day, whether or not to participate in the Demand Response (DR) event, and then optimizes the electric consumption to increase savings. A simulation study is carried out using a building model extracted from an EnergyPlus model, real measured weather data, and real day-ahead spot market price data for New York. Savings in the range of 23% to 33% are reported.

Keywords: Control of renewable energy resources, Optimal operation and control of power systems, Modeling and simulation of power systems
Abstract: The paper introduces the Wavestar wave energy converter and presents the implementation of model predictive controller that maximizes the power generation. The ocean wave power is extracted using a hydraulic electric generator which is connected to an oscillating buoy. The power generator is an additive device attached to the buoy which may include damping, stiffness or similar terms hence will affect the dynamic motion of the buoy. Therefore such a device can be seen as a closed-loop controller. The objective of the wave energy converter is to harvest as much energy from sea as possible. The straight forward solution to this maximization problem is achieved by maximizing the instantaneous range of motion of the buoy. The buoy as a single degree of freedom oscillator will undergo its maximum movements when it is in resonance with the sea state. Hence the best solution to the problem is achieved by forcing this condition. In the paper the theoretical framework for this principal is shown. The optimal controller requires information of the sea state for infinite horizon which is not applicable. Model Predictive Controllers (MPC) can have finite horizon which crosses out this requirement. This approach is then taken into account and an MPC controller is designed for a model wave energy converter and implemented on a numerical example. Further, the power outtake of this controller is compared to the optimal controller as an indicator of the performance of the designed controller.

Keywords: Stability of nonlinear systems, Passivity-based control, Networked systems
Abstract: An attempt to evaluate accuracy of Fridman's sampling interval estimates for nonlinear discrete-continuous systems where the controlled plant belongs to a class of cascade passifiable Lurie systems. Numerical results obtained for master-slave configuration of two mobile robots demonstrate good accuracy of Fridman's estimates: error of the sampling interval estimate is less than 25% of the value obtained from extensive simulation. In contrast, the error obtained by conventional method from quadratic Lyapunov function is more than 75% of the value obtained from simulation.

Keywords: Passivity-based control, Application of nonlinear analysis and design, Nonlinear observers and filter design
Abstract: This paper studies a visual feedback 3D pose regulation problem explicitly handling camera frame rates. Although numerous works have already tackled vision-based estimation/control problems by focusing on the limitation of measured output (2D visual information), almost all of them have assumed that visual measurements of a camera are continuously available. However, camera frame rates and image processing time are often non-negligible compared with other computation time. In view of this fact, we newly propose a discrete visual feedback pose regulation law under the situation that visual measurements are sampled due to frame rates. We first give a sufficient condition of frame rates to achieve a desired relative pose to a static target object. Then, the tracking performance for a moving target is analyzed via the notion of ultimate boundedness. The present analysis provides a guideline for the design of estimation/control gains. We finally show the significance and validity of this work through 3D simulation.

Keywords: Passivity-based control, Lyapunov methods, Networked systems
Abstract: In this paper, passivity and passivation problems for event-triggered feedback interconnected systems are addressed. We consider passivity in two event-triggered control schemes based on the location of the event-triggered samplers: sampler at plant output and sampler at controller output. For both schemes, we first derive the conditions to characterize the level of passivity for the interconnected system using passivity indices. The event-triggering condition proposed guarantees that these indices can be achieved. Then the passivation problem is considered and passivation conditions are provided. The passivation conditions depend on the passivity indices of the plant and controller and also the event-triggering condition, which reveals the trade off between performance (passivity level) and communication resource utilization.

Keywords: Passivity-based control, Stability of nonlinear systems, Lyapunov methods
Abstract: This paper investigates the problem of passive control for a class of bilinear stochastic systems. It assumes that the multiple dimensional control is bounded. Based on storage function method, passivity condition for the bilinear stochastic systems is presented by introducing a bounded nonlinear feedback controller. Moreover, input and output strict stochastic passivity, the relationship between output strict stochastic passivity and stochastic L_2 gain from input to output, and stochastic KYP (Kalman-Yakubovich-Popov) property for bilinear stochastic systems have been considered. A numerical example is provided to show the effectiveness of the method.

Keywords: Lagrangian and Hamiltonian systems, Passivity-based control
Abstract: A well-known problem in the application of the Interconnection and Damping Assignment technique for the stabilization of underactuated mechanical systems is dissipation in unactuated coordinates, since it may impede the definiteness requirements for the closed-loop system. Recently, the expansion of the closed-loop Hamiltonian function by a cross term between coordinates and momenta has been explored showing promising results. However, the large number of free parameters is an issue for the tuning of the closed-loop system, and the solution of the matching partial differential equations (PDEs) remains a difficult task. In this work, we aim at giving the closed-loop augmented Hamiltonian more structure in order to simplify the controller parametrization. The result is desired behavior at the equilibrium avoiding the solution of PDEs. Simulations and experiments demonstrate the applicability of the method.

Keywords: Control of switched systems, Power systems
Abstract: In this article, the problem of hybrid optimal control for DC-DC power converters is treated. The designed control is of type bang-bang established from Pontryagin’s maximum principle. The control is a state feedback and it is determined using an energy based minimization criterion derived from the power balance of Port-Hamiltonian systems. The developed control has the advantage to be easy to design and simple to implement in real time applications. The proposed control is applied to a SEPIC converter and validated in simulation and experimentation.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Automotive sensors and actuators, Vehicle dynamic systems
Abstract: The paper gives an analysis of a variable-geometry suspension system. Since the system also affects both the wheel camber angle and the additional steering angle, the coordination of steering and wheel tilting can be handled by this system. Since the nonlinear effects of the system are significant, the paper gives a detailed set-based analysis for the possibilities and constrains. A nonlinear polynomial SOS (Sum-of-Squares) programming method is applied to present the advantages of the variable-geometry suspension system.

Keywords: Automotive system identification and modelling, Multi-vehicle systems, Safety
Abstract: This paper introduces the concept of active buckling control and how it can be applied to vehicle body structures to improve safety. Active buckling control is achieved through the use of actively controlled materials, whereby the mechanical properties of a structure can be altered. The need for active buckling control is prompted by compatibility issues arising when vehicles of dissimilar mass collide. Effectively, the active buckling control system can stiffen the more vulnerable vehicle, as a result, sharing the collision energy more appropriately and improving the safety of the occupants. A model of a nonlinear force versus deformation characteristic is used within a simulation study to demonstrate the active buckling control concept; thereby reducing the undesirable energy distribution so that the collision energy is absorbed more appropriately.

Keywords: Control architectures in automotive control, Vehicle dynamic systems, Automotive sensors and actuators
Abstract: Using Lyapunov theory, Pontryagin’s minimum principle, and affine quadratic stability, a novel robust optimal control strategy is developed for active suspension systems to enhance vehicle ride comfort and handling performance. The controller has a simple structure, making its suitable for real-time implementation. The required sensor configuration includes a six-axis IMU and four LVDTs. The proposed controller is suitable for on-road commercial vehicles where ride comfort over bump disturbances and handling performance are the most concerns. The effectiveness of the controller is verified through simulation results using IPG CarMaker software.

Keywords: Control architectures in automotive control, Vehicle dynamic systems, Modeling, supervision, control and diagnosis of automotive systems
Abstract: A hierarchical control architecture for lateral stabilization in automotive vehicles is proposed in this paper. The proposed direct yaw stabilization strategy is based on the optimal coordination of two conventional stabilization systems (differential brake and traction torque transfer). Thanks to this approach, high stabilization capabilities are obtained while the impact on the longitudinal motion is reduced. The control architecture is a three layer structure composed of a high-level control, an optimal control allocation and a low-level control. The main idea consists in the use of a dual-mode control allocation formulation to handle the different operating modes of the wheel according to available actuators, i.e. conventional braking and/or torque transfer. The performance of the integrated control strategy proposed is highlighted through simulation results.

Keywords: Vehicle dynamic systems, Automotive sensors and actuators
Abstract: This paper investigates the application of semi-active inerter in semi-active suspension. A semi-active inerter is defined as an inerter whose inertance can be adjusted within a finite bandwidth by on-line control actions. A force-tracking approach to designing semi-active suspension with a semi-active inerter and a semi-active damper is proposed, where the target active control force derived by LQR control in the "Reciprocal State-Space" (RSS) framework is tracked by controlling the semi-active damping coefficient and semi-active inertance. One of the advantages of the proposed method is that it is straightforward to use the acceleration information in the controller design. Simulation results demonstrate that the semi-active suspension with a semi-active inerter and a semi-active damper can track the target active control force much better than the conventional semi-active suspension (which only contains a semi-active damper) does. As a consequence, the overall performance in ride comfort, suspension deflection and road holding is improved, which effectively demonstrates the necessity and the benefit of introducing semi-active inerter in vehicle suspension.

Keywords: Vehicle dynamic systems, Kalman filtering techniques in automotive control, Automotive system identification and modelling
Abstract: We present a new nonlinear state estimation approach based on Kalman filter theory and Takagi-Sugeno (TS) modeling for an active vehicle suspension application in this paper. The nonlinear state equations of a so-called hybrid suspension configuration, which result from nonlinear spring and damping characteristics, are exactly represented by means of a continuous-time TS system, i.e. a convex combination of local linear state space models. We derive observer gain matrices for each linear subsystem on the basis of standard Kalman filter theory, before we construct the global observer for the overall nonlinear system. Convergence of the global observer is ensured in terms of linear matrix inequality conditions. We then study the estimation performance of the TS Kalman filter in simulations and experiments on a hybrid quarter-car test rig using a measured road profile as disturbance input. The approach achieves a high estimation accuracy of well above 90% in the simulation and 70 − 90% in the experiments.

Keywords: Smart grids
Abstract: Electric power grids have been identified as critical infrastructures. They are increasingly dependent on Information and Communication Technologies (ICTs) for the operation and control of physical facilities. It can be envisioned that on top of the power infrastructure reside ICT layers that are coupled with the electric grids. As the ICT connectivity increases, so does the potential for cyber intrusions. This paper describes the importance of cyber security for power systems. A testbed architecture provides an accurate and powerful tool for identification of cyber-physical system vulnerabilities, security enhancement, impact analysis, and mitigation of cyber attacks. Simulation scenarios of cyber intrusions and attacks on the power grid, using the testbed, are discussed. The impact analysis’ simulation results capture the dynamic behavior of IEEE 39-bus system as a response to cyber attacks which may evolve into a partial or complete blackout. The problem of fast restoration from blackout after cyber attacks is identified.

Keywords: Smart grids, Constraint and security monitoring and control, Optimal operation and control of power systems
Abstract: The increasing and intensive integration of distributed energy resources into distribution systems requires adequate methodologies to ensure a secure operation according to the smart grid paradigm. In this context, SCADA (Supervisory Control and Data Acquisition) systems are an essential infrastructure. This paper presents a conceptual design of a communication and resources management scheme based on an intelligent SCADA with a decentralized, flexible, and intelligent approach, adaptive to the context (context awareness). The methodology is used to support the energy resource management considering all the involved costs, power flows, and electricity prices leading to the network reconfiguration. The methodology also addresses the definition of the information access permissions of each player to each resource. The paper includes a 33-bus network used in a case study that considers an intensive use of distributed energy resources in five distinct implemented operation contexts.

Keywords: Smart grids, Intelligent control of power systems, Distribution automation
Abstract: As pointed out in cite{4118454}, in the three decades since researchers at Bosch GmbH launched the technology of networked control systems for automobiles, there has been an explosion of interest in both the theory and deployment of real-time networks of devices. This interest is especially apparent in much of the current research on the smart grid. In what follows, recent work to illuminate the challenges and benefits of various communication and control protocols within pools of networked energy consuming devices and energy providers is discussed. We compare what can be possibly achieved in demand side management under two different protocols between the smart building operator [SBO] and the distributed energy consuming appliances. In the first protocol carrying the complete appliances' state information, the SBO is able to design a control law to reduce consumption uncertainty and to guarantee consumers' satisfaction. In the second protocol that communicates binary appliances' information, the scheduling performance reflects lower consumer utility and greater consumption uncertainty. In addition, we discuss the optimal energy reserve purchasing strategy for the SBO that strikes a balance between having excess capacity and having energy deficiency. Numerical simulation illustrates the protocols.

Keywords: Smart grids, Monitoring and performance assessment
Abstract: In recent years, the increasing capabilities of current technologies have made the acquisition and storage of large datasets an easy task. However, most times their unmanageable size, along with their complexity makes it a challenge to handle and analyze them. The emerging field of visual analytics relies on visualization principles and interactive interfaces to provide ways for amplifying human cognition, so that the information processing is improved and a better understanding of these datasets is achieved. In addition, current technologies to develop visual interfaces allow to develop powerful interaction mechanisms that enable the user to be an active part in the process. In this paper, data visualization foundations and web-based methods are considered for user-driven supervision tasks in decision-support systems. A real case consisting in the analysis of electric power demand in university buildings is presented via a web application developed using the recent data visualization library called D3. Time-series visualization and similarity maps are represented along with interactive techniques, allowing a dynamic data exploration.

Keywords: Modeling and simulation of power systems, Smart grids, Real time simulation and dispatching
Abstract: The complexity of modern day power grid is increasing with penetration of large-scale renewable resources at various levels of the networks. Inclusion of communication networks, to facilitate data transfer for ensuring reliability and security of the power system, with the electricity network extends the complexity to a new scale in a contemporary smart power grid. Any error in the integrated communication network can propagate through the electricity grid initiating large-scale cascading outages. Extensive precautionary measures should be taken in order to avoid such catastrophe. Complex network theory has been very useful in identifying critical components within the system as well as modelling of cascading failures. Complex network theory based percolation methods have been proved to be very useful in assessing power grid vulnerability under random and targeted attacks. In this paper, we extend our network percolation based analysis to explore the functional vulnerability of the existing electricity grid, effect on grid service efficiency, of increasing penetration of renewable energy. Vulnerable regions are identified with a fast computer simulation which helps operators to take quick preventive measures in case of emergency. Various standard IEEE test systems are simulated to test the applicability of the proposed algorithm in a real power system.

Keywords: Optimal operation and control of power systems, Constraint and security monitoring and control, Smart grids
Abstract: The state estimators used in real-time power system control centers now process bad data as a standard routine. With the introduction and deployment of phasor measurement units (PMUs), it is possible to model power systems, even with their time-varying nature, in real-time. However, PMUs remain vulnerable to providing bad data for several reasons. In this paper, a new intelligent framework, the cellular computational network (CCN), is introduced for the decentralized predictive modeling and dynamic state estimation (DSE) of a power system with PMU data. The CCN-based DSE is resilient to interactions between multiple segments of bad data from one or more PMUs.

Keywords: Autotuning, Identification for control
Abstract: In this paper, we propose a data-based auto-tuning method for industrial PID controllers, which does not rely on a model of the plant. The method is inspired by the Virtual Reference Feedback Tuning approach for data-based controller tuning, but it is taylored to the framework of PID controller design. The method is entirely developed in a deterministic, continuous time setting and no assumptions on stationarity of signals or persistency of excitation are made. The effectiveness of the proposed approach is tested on a benchmark example, that has been recently proposed for the evaluation of PID controllers

Keywords: Autotuning, Iterative modelling and control design
Abstract: An automatic controller tuning approach is presented that iteratively updates a linear quadratic regulator (LQR) design such that the resulting controller achieves improved closed-loop performance. In each iteration, an updated LQR gain is obtained by adjusting the weighting matrices of the associated quadratic cost. The performance of the resulting controller (measured in terms of another quadratic cost with fixed weights) is evaluated from experimental data obtained by testing the controller in closed-loop operation. The weight adjustment occurs through a stochastic optimization seeking to minimize the experimental cost. Simulation results of a stochastic linear system show that the self-tuning algorithm can recover optimal performance despite having imprecise model knowledge. Experiments on an inverted pendulum demonstrate that the method is effective in improving the system's balancing performance.

Keywords: Autotuning
Abstract: The relay autotuner for PID control is based on the simple idea of investigating process dynamics by the oscillation obtained when the PID controller is replaced by a relay function. In this paper an asymmetric relay function is used, which provides an equation for the static gain of the process. A method to find the normalized time delay is proposed and the benefits of this is discussed. Ways to find low-order models from the experiment are described. Considerations of how to choose the relay parameters are made and some examples are given.

Keywords: Adaptive system and control, Nonlinear adaptive control, Autotuning
Abstract: We present a new stabilization approach for unstable linear plants with long input delay, unknown parameters and disturbance. The predictor-based algorithm providing plant identification and stabilization is proposed. Also the extension is considered with estimation and cancellation of an unknown disturbance. A numerical examples are given to illustrate the efficiency of our adaptive controller.

Keywords: Nonlinear adaptive control, Fault detection and diagnosis, Adaptive control -applications
Abstract: In [18], a class of nonlinear systems with stochastic actuator failures have been studied. Stochastic functions related to Markovian variables have been introduced to describe the failure scaling factors for each actuators. In this paper we consider the kind of systems involving state-delay, which is much more practical, and adaptive fault-tolerant control problem has been studied. Two main challenges arise: how to establish the infinitesimal generator for systems involving multi-Markovian variables and state-delay simultaneously; then how to handle the delayed terms and the extra transition rate related ones appearing in the wisely designed Lyapunov-Krasovskii functional. By proposing an adaptive fault-tolerant control scheme, the existence and uniqueness of the solution process to the closed-loop system is guaranteed and the boundedness in probability of all the closed-loop signals can be ensured. An example is worked out to illustrate the effectiveness of the proposed scheme.

Keywords: Robust adaptive control, Adaptive control by neural networks, Discrete event modeling and simulation
Abstract: This paper deals with the application of Echo State Network (ESN) model to robust control of the Twin Rotor Aero-Dynamical System (TRAS) through estimation and cancellation of disturbances. The work describes the modelling process of the plant and control scenarios in which the system is under influence of the unknown disturbances. In such control scenarios the ESN model is used to estimate the disturbances and calculate the input correction in such way to improve the control quality. All data used in experiments are collected from the TRAS through the Matlab/Simulink environment.

Keywords: Automatic control, optimization, real-time operations in transportation, Modeling and simulation of transportation systems, Intelligent transportation systems
Abstract: It has been recognized that model predictive control (MPC) approach can be successfully applied to the signal control of the urban traffic systems. In this research, we mainly focus on dealing with the uncertainty of the inflow to the traffic network based on the MPC method. The stochastic process describing the uncertainty and chance constraints are embedded to the mathematic programming problem to prevent the congestion happening on the arteries. A modified MPC algorithm is also developed to solve the novel problem under studies. The simulation results show that the proposed model is closer to the real situation than the deterministic ones. The novel MPC algorithm strictly keeps the traffic flows below the limit of the arteries. Moreover, from computation point of view, the proposed method requires shorter computation time that may meet the real-time control requirement.

Keywords: Information processing and decision support, Modeling and simulation of transportation systems, Automotive sensors and actuators
Abstract: This paper introduces a topic which is of growing interest in the traffic control and management community, namely the relation between traffic data quality and the efficiency of traffic management. After some explanatory background and state of the art, it will become clear that no standard approach is available for traffic engineers to determine which accuracy of traffic data is needed for a certain traffic management application. In this paper we will show how the effect of inaccurate measurements on network level can be quantified by determining information utility functions. This approach is illustrated by presenting the results of a simulation study into the effect of inaccurate loop detector measurements on a ramp metering system. The relation between different accuracies of flow and speed measurements as input for a ramp metering system and the corresponding effect on the network performance is studied with a micro simulation model. The results of the simulation are used in a cost-benefit analysis for a case comparing loop detectors with camera measurements, where loop detectors are considered more accurate but also more expensive. We will show that higher measurement accuracy will lead to a better performance of the ramp metering system, resulting in a higher average network speed, and also quantify this relationship.

Keywords: Automatic control, optimization, real-time operations in transportation, Intelligent transportation systems, Modeling and simulation of transportation systems
Abstract: This work presents an algorithm for the optimization of urban traffic flow that computes the vehicle routes and traffic lights timing in real time. The optimization procedure uses a genetic algorithm whose fitness function consists of a high-level Petri net model of the urban traffic flow, which simulation results in the fitness value to be used. The outcome of this work is the optimization of urban traffic flow by simultaneously establishing the best possible routes for each vehicle and the definition of the most appropriate traffic lights timing. According to the tests, the simultaneous optimization of traffic lights time and vehicle routes decreased the total travel time as compared to the optimizations performed considering only the routes.

Keywords: Intelligent transportation systems, Modeling and simulation of transportation systems
Abstract: The control of a network of signalized intersections is considered. Previous works proposed a feedback control belonging to the family of the so-called back-pressure controls that ensures provably maximum stability given pre-specified routing probabilities. However, this optimal back-pressure controller (BP*) requires routing rates and a measure of the number of vehicles queuing at a node for each possible routing decision. It is an idealistic assumption for our application since vehicles (going straight, turning left/right) are all gathered in the same lane apart from the proximity of the intersection and cameras can only give estimations of the aggregated queue length. In this paper, we present a back-pressure traffic signal controller (BP) that does not require routing rates, it requires only aggregated queue lengths estimation (without direction information) and loop detectors at the stop line for each possible direction. A theoretical result on the Lyapunov drift in heavy load conditions under BP control is provided and tends to indicate that BP should have good stability properties. Simulations confirm this and show that BP stabilizes the queuing network in a significant part of the capacity region.

Keywords: Modeling and simulation of transportation systems, Intelligent transportation systems
Abstract: A novel model based local ramp metering method is presented in the paper by means of incident scheduled freeway traffic control solution. First, second order macroscopic freeway model is used with appropriate incident parametrization to describe eventual and unattended traffic variation caused by off-nominal traffic conditions (e.g. accidents). These traffic anomalies are captured by adequate model parameters, i.e. incident parameters that can be on-line estimated. The paper is motivated by incident scheduled ramp-meter solution to encounter real-time incident parameter information. The main idea is to use local freeway control solution triggered by available incident parameter values. The proposed approach is local in the sense of considering only non-coordinated ramp meter solution, first. Furthermore, we apply locally optimal (linearized) control solution to satisfy throughput maximization objective. The formal controller synthesis involves parameters to correct, compensate the effect of incidents. The proposed method is evaluated and compared to other existing approach by using simulation environment.

Keywords: Automatic control, optimization, real-time operations in transportation, Intelligent transportation systems
Abstract: Mainstream traffic flow control (MTFC) enabled via variable speed limits (VSLs) has been investigated in previous studies, utilizing various control strategies. In this paper a new feedback control strategy is proposed for MTFC enabled via VSLs, considering multiple bottleneck locations. Results are evaluated using a validated macroscopic model. The feedback concept is robust and can be immediately implemented in the field as it considers practical and safety constraints.

Keywords: Industrial applications of optimal control, Static optimization problems, Large scale optimization problems
Abstract: Achieving illumination and energy consumption targets is essential in indoor lighting design. The provision of localized illumination to occupants, and the utilization of natural light and energy-efficient light-emitting diode (LED) luminaires can help meet both objectives. Localized illumination control schemes require suitable coordination mechanisms to obtain luminaire dimming levels that achieve the desired illuminance levels and reduce energy costs. This paper presents several distributed optimal LED-based illumination control schemes that provide localized illuminance to occupants. The lighting system consists of multiple LED-based luminaires, each of which has a controller that can process information locally and communicate with nearby controllers. The illuminance requirements and energy costs for the lighting system are expressed as a linear programming problem. This optimization problem is solved, in a distributed manner, across the network of controllers using local communication amongst the controllers. State-of-the-art distributed optimization methods, based on accelerated first-order methods, are applied to parallelize the computational tasks among multiple controllers. Important practical aspects such as the rate of convergence, computational complexity, and communication requirements are investigated via simulations.

Keywords: Optimal control theory, control of fluid flows and fluids-structures interactions, Model reduction
Abstract: This paper proposes a suboptimal feedback controller design for nonlinear distributed parameter systems via the stable manifold method. The stable manifold method provides approximate stabilizing solutions of Hamilton-Jacobi (HJ) equations in nonlinear optimal control theory. We apply this method to a reduced-order system obtained from the proper orthogonal decomposition (POD) and Galerkin projection. The feasibility of the design is demonstrated by the numerical example of feedback controls of a Burgers’ equation.

Keywords: Control of partial differential equations, Disturbance rejection (linear case)
Abstract: We consider boundary stabilization for a multi-dimensional wave equation with boundary control matched disturbance that depends on both time and spatial variables. The active disturbance rejection control (ADRC) approach is adopted in investigation. An disturbance estimator is designed to estimate, in real time, the disturbance, and the disturbance is canceled in the feedback loop with its approximation. All subsystems in the closed-loop are shown to be asymptotically stable. The numerical experiments are carried out to illustrate the convergence and effect of peaking value reduction.

Keywords: Control of partial differential equations
Abstract: The aim of this work is to study of the numerical approximation of the controls for the hinged beam equation. A consequence of the numerical spurious high frequencies is the lack of the uniform controllability property of the semi-discrete model for the beam equation, in the classical setting. We solve this deficiency by adding a vanishing numerical viscosity term, which will damp out these high frequencies. An approximation algorithm based on the conjugate gradient method and some numerical experiments are presented.

Keywords: Control of partial differential equations, Sliding mode control, Infinite-dimensional systems
Abstract: This paper is focused on the design of robust tracking control for a class of reaction-diffusion partial differential equations. Both uncertainties in the model parameters and bounded external perturbations are considered. Global practical stabilization is addressed through a regularized sliding-mode approach; in addition, a scheduled controller is proposed in order to achieve asymptotic stability if some conditions on the reference to be tracked are met. Numerical simulations support theoretical results.

Keywords: Generalized solutions of Hamilton-Jacobi equations, Hamiltonian trajectories in optimal control, Optimal control theory
Abstract: We introduced notions of generalized solutions of PDEs of the first order (Hamilton-Jacobi-Bellman equation and a quasi-linear hyperbolic system of the first order). Connections between the solutions and the value functions to optimal control problems are studied. We described properties of the solutions with the help of Cauchy characteristics method.

Keywords: backstepping control of distributed parameter systems, Disturbance rejection (linear case), semigroup and operator theory
Abstract: In this paper, active disturbance rejection control (ADRC) approach is used to stabilize a 2*2 system of first-order linear hyperbolic partial differential equations (PDEs) subject to a boundary input disturbance. Disturbance attenuation is achieved with the designed controller, and the resulting closed-loop control system admits a unique solution, which could tend to any arbitrary vicinity of zero.

Keywords: system identification and adaptive control of distributed parameter systems, control of fluid flows and fluids-structures interactions, backstepping control of distributed parameter systems
Abstract: We present an adaptive observer design for a first-order hyperbolic system of Partial Differential Equations with uncertain boundary parameters. The design relies on boundary measurements only, and is based on a backstepping approach. Using a Gradient Descent technique, we prove exponential convergence of the distributed system and estimation of the parameter. This method is applied to the estimation of uncertain parameters during the process of oil well drilling.

Keywords: port Hamiltonian distributed parameter systems, Control of constrained systems, stability of distributed parameter systems
Abstract: Abstract The IDA-PBC control of plasma dynamics in a tokamak is investigated. It is based on a model made of the two coupled PDEs of resistive diffusion for the magnetic poloidal flux and of radial thermal diffusion. The used Thermal-Magneto-Hydro-Dynamics (TMHD) couplings are the Lorentz forces (with non-uniform resistivity) and the bootstrap current. The control model is obtained with the coupling of the two finite dimensional approximations obtained from the two diffusion models, using two geometric reduction schemes. A feedforward control is used to ensure the compatibility with the actuator physical ability. Then, an IDA-PBC (Interconnection and Damping Assignment - Passivity Based Control) controller is proposed for the coupled model to improve the system stabilization and convergence speed. The obtained numerical results are validated against the simulation data's obtained from the RAPTOR (Plasma Transport simulatOR) code for the TCV (Tokamak ï¿œof Configuration Variable at CRPP, EPFL, Lausanne, Switzerland) tokamak real-time control system.

Keywords: model reduction of distributed parameter systems, port Hamiltonian distributed parameter systems, Descriptor systems
Abstract: This paper proposes a method of balanced model reduction for constrained linear port Hamiltonian systems. Constrained linear port Hamiltonian systems are first written in a canonical descriptor form such that the Hamiltonian structure is preserved. The computations of the controllability and observability Gramians are then used to derive the balanced port Hamiltonian representation of the system. The method of flow constraint is applied to reduce the system. Finally, numerical simulations for the reduction of a micro mechanical actuator model is given to illustrate the effectiveness of the proposed method.

Keywords: delay compensation for linear and nonlinear systems, backstepping control of distributed parameter systems, Robust time-delay systems
Abstract: This paper addresses the design of a robust prediction-based controller for linear systems with both input and state delays. We extend the usual prediction-based scheme to state delay and prove its robustness to sufficiently small delay mismatches. Our approach is grounded on the linking of two recently proposed infinite-dimensional techniques: a Complete-Type Lyapunov functional, which enables state delay systems stability analysis, and tools from the field of Partial Differential Equations, reformulating the delays as transport equations and introducing a tailored backstepping transformation. We illustrate the merits of the proposed technique with simulations on a process dryer system.

Keywords: Ethics, Systems Theory, Complexity modelling
Abstract: This keynote paper provides a control systems perspective of failed internationalised institutional arrangements. In the past years we have experienced dramatic international systems failure across important institutions. Banks, religious institutions and governmental systems have all experienced catastrophic crises which have proven difficult to assess and address. This paper examines these failures as complex systems failures and highlights the role of socio-cultural systems in their failure. Firstly, the institutional arrangements are formally defined as complex systems. Secondly, the paper proposes the systemic failure as a failure of institutional culture which can be accounted for by certain systemic aspects of the cultural systems which underpin institutional life. This contribution utilises a three layer model of culture to demonstrate systems effects in the institutional arrangements. The paper then presents empirical findings from a study of a large scale medical system implementation to demonstrate the role of cultural symbolic factors in the system implementation.

Keywords: Ethics, Culture, Ethnicity
Abstract: Robotics is a very fast growing field especially in the last years and is a discipline based on: Mechanics, Physics/Mathematics, Control Engineering, Electr(on)ics, Computer Science. Therefore robots are frequently used as examples for Mechatronic Systems. Robotics unifies two cultures: Science and Humanities. The effort to design Roboethics should make the unity of these two cultures a primary assumption. This means that experts shall view Robotics as a whole - in spite of the current early stage which recalls a melting pot. Some decades ago social aspects of robotics were discussed. Because of the results and the rapid development of this field ethical issues became more and more important. Therefore the term Roboethics was introduced in the literature. In this contribution an overview from a practical viewpoint will be given. Starting with a short presentation of currently and in the future available robots some ideas about the ethical problems are discussed. Special emphasis is on the ethical behavior of the system “human-robot” and the “End of Life – EoL” management.

Keywords: Ethics, Culture, Management of technology developments
Abstract: Modern technology is complex and interdisciplinary, used by many sectors, with an impact on everybody regardless of cultural background or societal status. Increasingly, debates are arising concerning the proper use of technology and the need for ethical considerations. The most recent technological developments concerning nanotechnology and related technologies have lead to a multitude of questions and concerns regarding the potential environmental, economic, and societal risks and how we manage these potential risks. This study seeks to explore the potential challenges of nanotechnology and the effects of the nanotechnological applications and developments in indirectly reinforcing global inequalities.

Keywords: Ethics, Digital Divide, Technology Transfer
Abstract: Digital Data Collection (DDC) is an increasingly common method for data collection in developing country contexts, presenting both challenges and benefits to development practitioners and researchers. This paper explores the advantages gained and the difficulties encountered in transitioning from paper-based surveys to digital data collection using handheld devices, and some of the consequences for participants in the research, both researchers and the target population. Surveys undertaken as part of a research collaboration between University College of Cork and organisations in Malawi and Ethiopia form the basis of this assessment , with of a survey-based impact assessment study of Valid Nutrition’s Groundnut Purchasing Scheme with Smallholder Producers in Malawi and Ethiopia forming the centrepiece. The researchers have evaluated data-gathering on these different technologies, reflected on the methods used and approaches taken in transferring and implementing the process, and evaluated each process in terms of its relative effectiveness, efficiency and their implications for researchers and researched.

Keywords: Computational social systems, Control in economics, Ethical challenges and issues in social networking
Abstract: CIMMs (Computer Integrated Manufacturing systems) are important systems for overall automation of manufacturing enterprises, especially big ones. The general issues of the computer system topology were analyzed earlier. The present case study concerns the detailed design of the optimum LAN for the CIMMs sphere, based on the actual data for a big Polish household appliance manufacturer. For the data, the optimal LAN topology is determined, assuming development of democratic networks. In addition, the optimal topology for the general case is derived.

Keywords: Analysis of reliability and safety, Parameter estimation based methods for FDI
Abstract: To avoid structural damage, a wind turbine is equipped with a safety supervisor that triggers an emergency shutdown procedure in case of internal faults or large wind gusts. This paper leverages the (compositional) barrier certificate framework for the design and optimization of such a supervisor. The problem is formulated as a sum of squares problem and is solved using semi-definite programming. Both a direct and compositional approach are successfully implemented and verified for the NREL 5MW reference turbine. In conclusion, the methods derived in this paper are indeed viable for the syntheses and optimization of safety systems for future wind turbines.

Keywords: Distributed fault detection and isolation, Observer based and parity space based methods for FDI, Statistical methods/signal analysis for FDI
Abstract: Consensus and diusion based observers have been developed to address distributed state estimation over a sensor network. In this paper, the way sensor faults aect the state estimation of such distributed state observers is investigated. Next, a methodology to design a distributed system for sensor fault detection and isolation (FDI) is proposed. The aim is to achieve a specied fault detection (FD) performance while limiting the complexity of the algorithm at each node of the sensor network. To this end, proper selection of the measurements used for FDI at each node is performed thanks to a fault distinguishability measure based on the Kullback-Leibler divergence.

Keywords: Estimation and fault detection, Pattern recognition based methods for FDI, Fault diagnosis and fault tolerant control
Abstract: All process measurements are corrupted by the presence of measurement error to some degree. The attenuation of the measurement error, especially large gross errors, can increase the value of the process measurements. Gross error detection has typically been performed through rigorous statistical hypothesis testing. The assumptions required to derive the necessary statistical properties are restrictive, which lead to investigation of alternative approaches, such as artificial neural networks. This paper reports the results of an investigation into the utility of classification trees and linear and quadratic classification functions for resolving the gross error detection and identification problems.

Keywords: Maintenance strategies, Statistical methods/signal analysis for FDI, Pattern recognition based methods for FDI
Abstract: With the rapid growth of wind energy installed capacity, optimized maintenance has gained increasingly attentions from both researchers and wind farm owners. Condition- based maintenance (CBM) has been introduced to the wind energy industry in order to ensure the availability and safety of the wind energy conversion (WEC) system, while minimize the operating and maintenance (O&M) costs. In this paper a maintenance decision support system is introduced. By combining the information delivered by the data-driven WEC condition monitoring system and the economical benefits of each possible corrective maintenance action, the decision support system provides the operators with a choice of the most proper maintenance action for the current situation. The performance of the decision support system is tested with data collected from different WECs in a wind farm.

Keywords: Observer based and parity space based methods for FDI, Maintenance strategies
Abstract: This paper presents the simulation testing of a model-based condition monitoring scheme for the Joint European Torus neutral beam cryogenic pumping system. The scheme was tested by examining its response to a range of realistic simulated faults. Ten faults were tested. The scheme detected each of the faults and uniquely identified six of them. The scheme was shown to have an improved performance compared to the current manual detection method.

Keywords: Pattern recognition based methods for FDI, Statistical methods/signal analysis for FDI, Distributed fault detection and isolation
Abstract: Non-uniform current density distribution in PEM fuel cell results in local over-heating, accelerated ageing, and lower power output. This paper proposes a diagnosis approach for PEM fuel cell system based on monitoring the current density distribution inside the stack. A magnetic sensor network has been used to provide an image of the current density distribution. The diagnosis method has three steps: residuals generation, residual analysis to obtain symptoms and decision by classifying these symptoms. The proposed approach has been applied on virtual measurements and validated on experimental measurements under performance degradation due to long term functioning of the PEM fuel cell stack and under low air stoichiometric ratio due to an actuator fault. Results show that the proposed method is a remarkable tool for diagnosis and taking compensatory actions.

Keywords: Expert systems in process industry, Fault diagnosis and fault tolerant control, Identification and modelling
Abstract: The wide plant control integration of mining, mineral and metal processes poses many challenges. Testing strategies for process monitoring, diagnosis and control of flotation processes is expensive, and usually requires either complex pilot plant facilities or to deal with the hard constraints posed by experimentation in real plants. One critical aspect to take advantage of the present technological developments is the training of engineers to understand and master when and how some technique may be appropriate to solve a particular problem and to identify the challenges implicitly underlying in its implementation. In this sense the use of hybrid laboratories is presented, where fully instrumented pilot plants are operated under PLC and DCS coupled on-line with phenomenological models, to recreate an environment as close as possible to industry.

Keywords: Model predictive and optimization-based control
Abstract: This paper presents a repetitive model predictive controller to reject periodic disturbances in industrial processes. The proposed technique uses a state space model with embedded repetitive action to integrate most important characteristics from both repetitive and predictive controllers. Thus, the obtained control strategy combines best characteristics from both repetitive and model predictive control. A simulation case study is presented to discuss several aspects related to disturbance rejection performance, parameters tuning and constraints effects, and to compare this new control strategy with traditional repetitive and model predictive controllers.

Keywords: Identification and modelling, Process observation and parameter estimation, Advanced process control
Abstract: This paper presents a closed-form expression for the model-plant mismatch (MPM) that may be present in a feedback control system. The main limitation on the expression is that the controller and plant models should be representable by means of transfer functions, i.e. they should be linear and time invariant. This includes a variety of controllers, among which the ubiquitous Proportional, Integral and Derivative (PID) controller. The expression can then be used to identify the true plant transfer function. The MPM expression is shown to work for single-input single-output as well as a multiple input multiple-output systems.

Keywords: Identification and modelling, Process observation and parameter estimation, Process optimisation
Abstract: This paper describes a static flowsheet simulation model developed for the pilot scale mineral beneficiation plant taken recently into use at University of Oulu, Finland. The mini-pilot plant aims to serve as a research platform for students and researchers from universities, mining companies, and other organizations in the field of mining and mineral engineering. Currently, the mini-pilot plant is set up according to the concentrator plant at the Pyhäsalmi deposit but can be configured for different types of ores and beneficiation processes as well. The simulator is built using the HSC Sim simulation software. The simulator will support research and development of the new mini-pilot line. It can also be used in process design and optimisation. This paper presents preliminary results demonstrating the great potential of the simulation model. The future aim is to convert the static model into dynamic operating mode and study different control scenarios of mineral beneficiation plants using the simulator.

Keywords: Model predictive and optimization-based control, Process control applications, Identification and modelling
Abstract: Energy consumption reduction strategies in the mining industry have increased in the last years due to the energy prices increments. Control strategies in the mining processes are one of the many ways to optimize energy consumption, especially if the strategies consider global optimization and ensure the stability of the system. Mineral grinding accounts for as much as 50% of the energy consumption in a mineral concentrator plant. For this reason a centralized Hybrid model predictive control (HMPC) scheme is presented; this control minimizes the specific energy consumption and stabilizes the plant by ensuring an output particle size contained on the mesh 65. This work shows that more complex control solutions can be applied in the grinding process, substituting conventional control strategies in a successful way, and decreasing the control strategy implementation complexity, since conventional control strategies rely on expert systems to handle discrete variables and events, and HMPC strategies allow the inclusion of discrete events both in the model and in the controller.

Keywords: Model formulation, experiment design, Modeling and identification, Identification and validation
Abstract: Signaling pathways depending on NF-κB, p53 and HSF1 transcription factors are the major components of cellular response to stress. NF-κB transcription factor regulates genes responsible for inflammation, immune response and cell survival. p53 either activates cell cycle arrest and DNA repair or induces apoptosis in cells with DNA damage. HSF1 is activated under stress conditions and induces the heat shock response. All three pathways are essential for cancer and other human diseases. Here we review biological data on these signal transduction pathways and introduce complex crosstalk between these regulatory circuits as an apparent challenge for mathematical modeling.

Keywords: Bioinformatics, Kinetic modeling and control of biological systems, Pharmacokinetics and drug delivery
Abstract: In this work we propose new approach to the cancer treatment. We postulate to consider cancer as a malfunction of the cells regulatory unit, and use control engineering and systems theory approaches to describe that malfunction. In our approach mathematical models were used for the description of system and its malfunction. Then we postulate to deliver a secondary control signal to the malfunctioning cells which role is to omit the damaged regions and return the cell to its normal parameters. This should results in a programmed death of the cancer cells. In this paper we introduce the cell as an object of control, describe p53 signaling pathway as an example of cellular regulatory unit, then discuss its possible malfunctions which result in cancer and describe available secondary control signals. Finally we present the results of our in silico experiments, which show the efficiency of the proposed secondary controls.

Keywords: Biomedical system modeling, simulation and visualization, Decision support and control, Dynamics and control
Abstract: We compare sufficient conditions for local controllability for a class of models of treatment response to antiangiogenic and combined anticancer therapies. The combined therapy is understood as combination of direct anticancer strategy e.g. chemotherapy and indirect modality (in this case antiangiogenic therapy). We discuss different control objectives related to multimodal therapies in the light of present knowledge of self-organization of tumor cell populations treated by anticancer drugs. Controllability of the models in the form of semilinear second order dynamic systems enables to expect that the objectives could be reached.

Keywords: Biomedical system modeling, simulation and visualization, Dynamics and control, Bioinformatics
Abstract: Evolutionary game theory has been widely used to simulate tumor processes. Inter-cellular interactions that occur within tumor populations are good examples of biological evolutionary games for space and nutrients. Presented extended model considers four strategies (phenotypes) that can arise by mutations: cells that produce harming substances to gain advantage, cells resistant to these substances, cells that produce growth factor which affects any other cell-kinds and neutral cells. Different equilibrium points, scenarios and spatial versions are also discussed.

Keywords: Model formulation, experiment design, Kinetic modeling and control of biological systems, Identification and validation
Abstract: We hypothesize that the GCSFR mutants introduce into those pathways changes that will lead to impaired differentiation and enhanced survival based on single cell behaviors as modulated by noise in intracellular proximal signaling and distal gene regulatory networks. We will show that GCSFR truncation mutants become fixed in the granulocyte progenitor population because of an incremental growth advantage. We will also show by modeling and experimentation that these GCSFR mutations alter the signaling pathways to further perturb growth, impair differentiation, and enhance survival.

Keywords: Bioinformatics, Modeling and identification, Data mining tools
Abstract: We developed a multi-null-hypotheses (MNH) method for testing signatures of natural selection in Kimura’s neutral model of human evolution with population growth and limited migration between dems. To evaluate the influence of such time changes and demography in population size, we employed different variants of the null hypothesis, corresponding to constancy, growth, or substructure and growth, respectively. We apply the model for searching in SNP haplotypes from four genes implicated in impairing DNA-repair mechanism in human familial cancers: ataxia telangiectasia (ATM), human helicase RECQL, Bloom’s syndrome (BLM) and Werner’s syndrome (WRN). The sample is composed of about 600 chromosomes, derived from residents of Houston, TX (USA), representing major ethnic backgrounds: Caucasian, African, Asian and Hispanic. The method is illustrated with Bloom’s neutrality tests B and Q. Our study suggests that detected deviations from neutrality may be obscured by presence of recombination, substructure and changes of population size in the case of RECQL haplotypes, while in the ATM haplotypes the signal is rather strong. The BLM and especially WRN haplotypes do not register deviations from neutrality.

Keywords: Autonomous vehicles in agriculture, Agricultural robotics
Abstract: The objective of this research was to develop an unmanned surface vehicle (USV) platform for autonomous navigation in the paddy field. The surface vehicle used in this research was a radio controlled air propeller vessel that had been modified into an unmanned surface vehicle platform. A GPS compass system was attached to the top of the USV platform as the navigation system to provide the position and heading angle. The USV platform can autonomously navigate to the predefined navigation map. From the GPS trajectory data of the map-based navigation experiment, the in-system root mean square (RMS) lateral error from the target path was observed to be less than 0.45 m, and the in-system RMS heading error was 4.4 degree or less. The final goal of the research is going to realize the autonomous weeding, intelligent fertilization or paddy growth management based on this USV platform.

Keywords: Agricultural robotics, Robotics and mechatronics for agricultural automation
Abstract: Automated agricultural equipment such as a robot tractor has been developed to solve the problem of agriculture labor force shortage. A human-driven tractor following a robot system is useful for saving time on a large-scale farm. In this kind of system, a robot tractor does farm work while a human-driven tractor is following and doing a different operation. The human operator can control the robot through a controller. To monitor work conditions of the leading robot tractor, four cameras are mounted on the robot tractor, and images from the cameras are sent to the human-driven tractor through a video transmission system. The human operator can check the surroundings of the robot from a monitor. Also, a laser scanner is mounted in the front of the robot for safety. The results of an experiment using the system showed that the precision of the robot tractor, which had an RMS error of about 0.03 m, was better than that of an experienced tractor operator, for which the RMS error was about 0.04 m, while the range of lateral errors of the human-driven tractor improved from 0.25 m to 0.20 m by following the robot.

Keywords: Robotics and mechatronics for agricultural automation, Modeling and control of agriculture, Man-machine systems in agriculture
Abstract: Three-point hitch is a mechanism used to connect an agricultural implement, e.g. a plow, rigidly to an agricultural tractor. Movement of a traditional three-point hitch provides only one degree of freedom. This limits the use of hitched implements and forces implement manufacturers to build additional hydraulics into the implement. In this research a tractor was instrumented to provide two rotational and two translational degrees of freedom for the rear hitch. The goal was to develop a control system which allows the operator to control the rear hitch in Cartesian coordinates. In this paper an iterative, damped least-squares (DLS) solution for the rear hitch inverse kinematic transform is presented. The method is tested on the instrumented tractor. The result is a functional control system allowing Cartesian control. The system also provides the rear hitch pose information via a virtual terminal (VT) interface.

Keywords: Robotics and mechatronics for agricultural automation, Agricultural robotics, Autonomous vehicles in agriculture
Abstract: This article described the development and utilization of a CAN-bus based robot tractor. A RTK-GPS and an IMU were utilized as a position and a posture sensor for the development of a navigation system. The path planning and navigation control method were described in this article. Three field tests were conducted to show the performance and stability of the robot on straight path, logged path following crop rows and long time work. The RMS value of lateral and heading errors of three tests were 0.05 m and 0.6 degree, 0.02 m and 0.77 degree, 0.04 m and 0.75 degree, respectively. The results revealed the CAN-bus based robot tractor has high and stable navigation accuracy. The accuracy was acceptable for all tests.

Keywords: Agricultural robotics, Autonomous vehicles in agriculture
Abstract: A 2D laser scanner was used for obstacle detection for autonomous vehicles for its high accuracy at both indoor and outdoor environment on the measurement of distance. Traditionally, the obstacle detection method was depended on the distance from an object to a laser scanner, and an obstacle was defined if the measured distance was less than a threshold. A shortage of the traditional obstacle detection method for a robot tractor is that a low obstacle cannot be detected when the height of the obstacle is lower than the height of the scanning plane of the 2D laser scanner. In this article, obstacle detection method using two 2D laser scanners is discussed. One laser scanner is mounted at the front and the other is mounted at the rear of the vehicle in order to cover the whole surroundings of the vehicle. The laser scanner was mounted look-at-down in order to detect a low obstacle. A test was carried out at an outdoor environment to verify the developed safety system. The test results indicate the developed system could detect obstacles with accuracy around 10 cm.

Keywords: Autonomous vehicles in agriculture, Man-machine systems in agriculture, Robotics and mechatronics for agricultural automation
Abstract: In this research a system assisting the operator of an agricultural tractor in coupling a trailer was developed. The system utilized knowledge of the dimensions of the trailer, width and drawbar length, in calculating the towing eyelet location from laser range finder measurements using iterative end-point fit. The tractors speed and curvature are then automatically controlled to align the draw hook with the towing eyelet. Curvature is controlled using pure pursuit algorithm and speed with Smith- predictor and P-controller. In final field tests the system provided 10 successful connections out of 10 attempts. Further research is required to make the system more general and robust to environmental anomalies. Overall performance was satisfactory.

Keywords: Modeling and identification, Physiological Model, Model formulation, experiment design
Abstract: Blood pressure (BP) is one of the principal vital signs. The regulation of normal BP is critical for maintaining the normal functioning of an organism. The structure of the BP regulation in vertebrates, including humans, is very complex and it is not yet fully understood. There are several BP regulation subsystems which interact together. In this study, we analysed and modelled the role of the most important BP regulation systems (sympathetic nervous system, renin-angiotensin-aldosterone system, L-Arginine/nitric oxide) as well as the role of reactive oxygen species, the imbalance of which is implied in the development of hypertension disease. Our aim is to design a dynamic model of the BP regulation in normotensive rats, which can help us better understand the complex process of BP regulation. The BP responses were measured in conscious rats after acute stress with or without the application of selected drugs, which can inhibit particular regulation subsystems. Linear dynamic models of particular regulation subsystems were identified using genetic algorithms. Each subsystem is a feedback loop, which contains both the system dynamics and the controller. We describe the simulation-based identification procedure of these particular models as well as of the entire BP regulation system.

Keywords: Quantification of physiological parameters for diagnosis and treatment assessment, Physiological Model, Bio-signals analysis and interpretation
Abstract: During and after liver-transplantation (LT) it is very difficult to maintain normoglycema. Blood glucose (BG) metabolism suffers from this huge disturbance and the pathological state changes very quickly. In addition there is further stress from the state of the graft liver and the host body’s ability to accept it. The number of physiological parameters, that can be observed is quite limited. Control can be implemented primarily by the administration of exogenous insulin and glucose. Thus, insulin sensitivity (SI) can be a key-parameter to observe physiological changes and develop patient and pathological state specific treatments. This study investigates the distribution of a validated, model-based SI over time and the main surgery phases in the LT.

Keywords: Physiological Model, Artificial pancreas or organs, Biomedical system modeling, simulation and visualization
Abstract: Mechanical circulatory assistance devices have been researched and used to assist the pumping function of the failing heart. Examples of these devices are intra-aortic balloon pumps (IABP) and ventricular assist devices (VAD). In order to perform the in vitro tests of these devices, before the in vivo tests, cardiovascular simulators are required. One important part of a cardiovascular simulator is the simulation of the left ventricle which is a pulsatile blood pump. Most of the existing left ventricle simulators use pneumatic actuator. This type of actuator creates negative pressure inside the ventricle chamber which is not physiological. The objective of this work is to develop a pulsatile pump that simulates the left ventricle without creating negative pressure. The concept of the pulsatile pump was evaluated using 3D printing to manufacture the prototype. The developed pulsatile pump is composed of two reservoirs: a rigid one with 5.0 x 104 mm3 of volume which corresponds to the residual systolic volume and a flexible one which ejects 8.0 x 104 mm3 of fluid per beating cycle when compressed by a platform. Two caged-ball valves simulating the mitral and the aortic valve are responsible for the unidirectional flow. A constant pressure of 1,333 Pa inside an open reservoir maintains the fluid flowing into the pulsatile pump through the mitral valve. A DC motor is connected to a lever mechanism that transforms the rotational movement of the motor into vertical movement of the platform. The vertical movement of the platform is responsible for the pulsatile flow of the pump, generating a pressure of 10,640 to 15,960 Pa at the outlet of the pump. Since the platform is not attached to the flexible reservoir, there is no generation of the negative pressure inside the simulator.

Keywords: Identification and validation, Parameter and state estimation, Pharmacokinetics and drug delivery
Abstract: This study focuses on the individual sedation model identication problem, and proposes a subspace-based Wiener system identication method. The traditional compartmental pharmacokinetics-pharmacodynamics hypnosis model is considered as a specic Wiener system with the Hill equation nonlinear term. To deal with the Hill nonlinear term, the proposed method employs a set of specic bases to turn the Wiener system into a linear one, and then the subspace orthogonal projection identication method has been implemented to identify the transformed linear model. Compared with the traditional anesthesia model identication methods, the proposed method can eectively overcome the shortage of measurement data, get rid of the estimation of the eect compartment concentration which is impossible to be measured, and improve the individualization performance of the identied model. A simulation study on 24 various virtual patients from theWang's Simulator has been conducted and validates the eciency and robustness of the proposed method, and a drug infusion instruction has been provided in order to get relatively accurate identication performance.

Keywords: Dynamics and control
Abstract: This paper presents an analysis of healthy immune system dynamics from the perspective of finite time stability. Finite time stability and stabilisation have been well-studied in various engineering applications. The principal paradigm uses non-Lipschitz functions of the states. Recent studies on robustness of closed-loop non-Lipschitz dynamical systems are based on Lyapunov functions and show that finite time stability is both a special case of and superior to the asymptotic stability of the system. Such a finite time convergence property has not been studied extensively in the area of biological systems where it is evident that the dynamics are finite time convergent to certain equilibrium points rather than asymptotically or exponentially convergent. Furthermore, there are examples such as a healthy immune system response where robustness is found to the state of auto-immune disease. These inherent features of robustness and finite time convergence motivate the development of a modified version of the Michaelis-Menten function, which is frequently used in biology, based on the finite time stability paradigm. The paper hypothesizes a potential connection between finite time stability and the dynamics of a healthy immune system.

Keywords: Control system design, Application of power electronics, Power systems stability
Abstract: This work develops a nonlinear controller for an induction heating system using a parallel resonant inverter. The proposed controller should be seen as a nonlinear alternative to the standard Phase-locked loop technique which uses a PI regulator. The asymptotic stability of the closed loop system is demonstrated using a Lyapunov function. The controller is experimentally validated and its performances (robustness and disturbance rejection) are compared to those of a PI regulator.

Keywords: Control system design, Modeling and simulation of power systems, Test and documentation
Abstract: This paper presents the design of a quasi optimal feedforward controller for a new type of high-voltage test systems. Compared to existing solutions, this test system has the advantage of a compact lightweight construction and is thus particularly suitable for on-site tests. For system analysis and controller design, a tailored mathematical model is derived, which describes the (slow) envelope dynamics of the occurring amplitude modulated signals. The proposed (quasi optimal) control concept ensures a simultaneous minimization of the mean power losses and of the distortion of the output voltage. Simulation results for a validated mathematical model show the feasibility of the approach.

Keywords: Process modeling and identification
Abstract: Desiccant wheels can improve the efficiency of air handling units, as their inherent heat recovery capabilities reduce the external energy needs. On the other hand, however, desiccant wheels introduce state variable interactions that are not present in traditional units. Hence, to actually yield the possible advantages, air handling units with desiccant wheels require accurate control, which in turn calls for dynamic wheel models that can be parametrised from design data, so as to allow for virtual prototyping, and couple reliability with numerical efficiency, to deliver the numerous system-level simulations required with an acceptable computational effort. This paper proposes a dynamic model for desiccant wheels fulfilling the above requirements. To support the proposal, examples ere reported on the use of the proposed model for the design and control of innovative unit layouts.

Keywords: Modeling and simulation of power systems, Human factors
Abstract: The complexity of modern process plants is steadily increasing – and thus the demands placed on automation systems. Today, the validation and testing of an automation system is a demanding task, usually executed under very tight schedules. Therefore, the use of simulations to check-out the automation system earlier in the engineering phase can be very beneficial. This paper investigates the possibility of automatically generating simulation models based on plant engineering data, usable for virtual commissioning. The proposed mechanism is usable without any specific additional application and can be realized with the plant engineering and simulation tool alone. The concept and workflow will be described in detail, and the paper concludes with a case study based on a prototypical implementation. Based on the proposed concept, the reuse of engineering data to generate simulation models, and thus the broader use of simulation throughout the lifecycle, becomes easier and therefore more feasible.

Keywords: Optimal operation and control of power systems, Control of renewable energy resources, Smart grids
Abstract: The recent rapid uptake of residential solar photovoltaic (PV) installations provides many challenges for electricity distribution networks designed for one-way power flow from the distribution company to the residential customer. In particular, for grid-connected installations, intermittent generation as well as large amounts of generation during low load periods can lead to a degradation of power quality and even outages due to overvoltage conditions. In this paper we present two approaches to mitigate these difficulties using small-scale distributed battery storage. The first is a decentralized model predictive control (MPC) approach while the second is a hierarchical distributed MPC approach using a so-called market maker. These approaches are validated and compared using data on load and generation profiles from customers in an Australian electricity distribution network.

Keywords: Fault detection and diagnosis, Estimation and filtering, Networked embedded control systems
Abstract: In this paper, a robust H_infinity fault detection filter is designed for a class of discrete-time nonlinear networked control systems via T-S fuzzy model with multiple bounded state delay and random packet dropout induced by the limited bandwidth of communication networks. Our aim in this paper is to analyze and design a robust H_infinity full-order fault detection filter, such that the filtering error dynamics is asymptotically mean-square stable with a prescribed H_infinity performance level. Sufficient conditions for the existence of the desired filter are presented. Finally, an example is given to illustrate the effectiveness and applicability of the proposed new design method.

Keywords: Model predictive and optimization-based control, Advanced control technology, Process modeling and identification
Abstract: System outputs with different sampling times may cause difficulties in subspace identification to obtain an accurate model when some system variables are sampled at faster rate. This identification problem is solved by dividing the multi-rate sampled system into different subsystems, and multi-rate distributed control is proposed to control such system by using the identified model.

Keywords: Nonlinear system identification, Hybrid and switched systems modeling, Discrete event modeling and simulation
Abstract: This paper deals with hybrid sliding mode control of Doubly Fed Induction Generator DFIG with Maximum Power Point Tracking MPPT connected by rotor side to three bridges of Multicellular Converters MCCs. The hybrid aspect of the converters is taken into consideration which includes the continuous and discrete states of the converters. The vector control is used to command the DFIG speed and reactive stator power. The currents in Park d-q reference are controlled using hybrid sliding mode. The sliding surfaces are developed using Lyapunov stability method. The developed controller allows decoupled control of the stator active and reactive power. The final results are illustrated at the end of this paper to present the advantages of the control method developed in this paper.

Keywords: Linear parametrically varying (LPV) methodologies, Switching control, Stability and stabilization of hybrid systems
Abstract: In this paper, virtual actuators are proposed as a Fault Tolerant Control (FTC) strategy for switching Linear Parameter Varying (LPV) systems subject to actuator faults. The overall solution relies on the addition of a virtual actuator block that keeps the stability and some desired performances without the need of retuning the nominal controller. It is shown that the design can be performed using polytopic techniques and Linear Matrix Inequalities (LMIs). Simulation results obtained with a four-wheeled omnidirectional mobile robot example are used to demonstrate the effectiveness of the proposed approach.

Keywords: Switching control, Hybrid and switched systems modeling
Abstract: This paper considers fault-tolerant control of a class of uncertain switched linear time-delay systems and its application to water pollution control. Due to the nature of average dwell-time techniques and the representation of actuator faults, this paper has the following features compared with the existing methods in the literature: 1) the proposed method is independent from switching polices provided that switching is on-the-average slow enough; 2) the proposed controller exponentially stabilizes this class of time-delay systems with actuator faults and its nominal systems (i.e., without actuator faults) without necessarily changing any structures and/or parameters of the proposed controllers; 3) the proposed method treats all actuators in a unified way without necessarily classifying all actuators into faulty actuators and healthy ones. Simulation results are provided to illustrate the effectiveness of the proposed method.

Keywords: Identification for control, Errors in variables identification
Abstract: In this paper we consider the problems of parameter estimation and model discrepancy for control systems governed by differential equations. We consider the case where one assumes both modeling and measurement errors. Modeling errors are an important source of model discrepancy which can greatly limit the usefulness of a model for prediction and design. Although Bayesian analysis is a powerful method for dealing with model discrepancy, it is well known that this approach tends to be very sensitive to prior assumptions about the model bias. We present an approach based on science based hierarchical modeling with uncertain disturbances to help develop prior knowledge about model discrepancy in order to improve the model’s predictive usefulness. We apply these ideas to examples involving control systems defined by ordinary differential and delay differential equations to illustrate the ideas and suggest future area of research.

Keywords: Distributed control and estimation, Event-based control, Control over networks
Abstract: The asymptotic stability of distributed-event triggered control for interconnected linear systems can be achieved with time-dependent trigger functions, whose parameters relies on some propoerties of the overall system, which are not known a priori. This paper presents an approach to the distributed estimation of such parameters, which yields to adaptive event-triggered control. We provide proofs of convergence in finite time while the asymptotic stability and the existence of a lower bound for the inter-event times are preserved.

Keywords: Multi-agent systems, Distributed control and estimation
Abstract: In this paper, a leader-following problem is considered for a group of autonomous agents when a second-order multi-agent system is used to describe the network dynamics. Both the dynamics of the leader and the followers are subjected to unknown disturbances, which are represented by linearly parameterized models. The relative position measurements and the relative velocity measurements are utilized as the new state variables to yield a new distributed system. When both the two relative measurements are available for followers, we design an adaptive tracking control for each follower, which needs no more global information about the bounds of the unknown disturbances. Moreover, analysis is also made of the stability of the tracking error system and the parameter convergence. A numerical example is finally given to illustrate the good performance of the proposed adaptive tracking control.

Keywords: Control of networks, Cooperative systems, Multi-agent systems
Abstract: In a resource limited multi-agent system, it is of practical importance to select a fraction of nodes (agents) to provide control inputs such that consensus can be achieved with optimized performance in terms of network cost and/or convergence speed. In this paper, we investigate the problem of how to select control nodes so as to minimize the network cost, where the control nodes are selected at the beginning and will be fixed all the time. This problem can be transformed to a combinatorial optimization problem, and further relaxed to a convex optimization problem with reweighted l_1 norm. We propose a suboptimal algorithm to solve the convex optimization problem. Finally, we offer several numerical examples to illustrate the efficiency of the proposed strategies, and investigate the relationship how the degrees of control nodes will influence network cost and convergence speed.

Keywords: Distributed control and estimation, Cooperative systems
Abstract: In this paper, a distributed partially cooperative control framework is proposed for a network of linear interconnected subsystems. It is assumed that each subsystem in the network possesses its own objective and a corresponding nominal interaction-free state feedback law. The proposed framework enables each subsystem to compute an additional control term in order to help maintaining the integrity of the overall network. As this cooperation-like behavior involves relative priority assignment, a communication aware heuristic is proposed with an associated stability assessment that is based on the closed-loop network matrix's spectrum monitoring. Illustrative examples are used to assess the effectiveness of the proposed scheme including a distributed load frequency problem.

Keywords: Multi-agent systems, Cooperative systems, Complex system management
Abstract: We present an opinion formation model in which individuals are supposed to seek a consensus on the truth. All agents are boundedly confident in the sense that an agent communicates with another one if their opinion difference is not greater than a threshold which is called the bound of confidence. When facing the truth, they are also boundedly confident such that they take the truth into account if the truth is inside their bound of confidence. Any agent influenced by the truth is viewed as a leader, and thus, the role of leader is endogenous with the evolution of opinions. For any pair of bound of confidence and position of truth, we provide a possible range of the strength of the attraction of truth that can guarantee the whole group converging to the truth. We also find that there always exists a suitable strength of the attraction of truth leading the whole group to a consensus on the truth as long as the whole group converge to a consensus in the absence of the attraction of truth.

Keywords: Multi-agent systems, Distributed control and estimation, Cooperative systems
Abstract: This paper describes a method to control the density distribution of a large number of autonomous agents. Our approach leverages from the fact that there are a large number of agents in the system, and hence the time evolution of the probabilistic density distribution of agents can be described as a Markov chain. Once this description is obtained, a Markov chain matrix is synthesized to drive the multi-agent system density to a desired steady-state density distribution, in a probabilistic sense, while satisfying some motion and conflict avoidance constraints. Later, we introduce an adaptive density control method based on real time density feedback to synthesize a time-varying Markov matrix, which leads to better convergence to the desired density distribution. This paper also introduces a decentralized density computation method, which guarantees that all agents will have a best, and common, density estimate in a finite, with an explicit bound, number of communication updates.

Keywords: Decoupling problems, Linear multivariable systems, Time-varying systems
Abstract: Using a simulated model of a house consisting of two adjacent rooms, the temperature of the two rooms is controlled with a PI controller and a decoupled PI controller. This is compared to MPC control. In the simulation example here, both the MPC controller and the decoupled PI controller decreased the interaction between the temperature dynamics of the two rooms, as compared to an independent PI controller in each room. For the example in this paper, the control performance of the decoupled PI controller is comparable to that of MPC, regarding the interactions between the two rooms.

Keywords: Observers for linear systems, Time-varying systems
Abstract: This paper describes a state estimation scheme for a class of linear system in which the sampling time of output measurement is multiple times of control period. Disturbance accommodation is applied to deal with model uncertainties and external disturbances. In order to maintain the correction stage between two consecutive updates of output, “pseudo measurement” is proposed. The effectiveness of the proposed estimation is verified by a case of application in vehicle state estimation using GPS.

Keywords: Robust control (linear case), Time-varying systems, Linear systems
Abstract: The robust stabilization of uncertain linear time-varying continuous-time systems with a mode-switch dynamics is considered. Each mode is characterized by a dynamical matrix containing elements whose time behavior over bounded time intervals is sufficiently smooth to be well described by interval polynomials of arbitrary degree. The stability conditions of the switching closed-loop system are derived defining a switched Lyapunov function and involving the dwell time of the system over each single mode. An important feature of the paper is that, unlike all the other existing methods, each plant mode can be stabilized over arbitrarily large uncertain domains of parameters and their derivatives.

Keywords: Time-varying systems, Linear systems, N-dimensional systems
Abstract: The Lyapunov, Bohl and Perron exponents belong to the most important numerical characteristics of dynamical systems used in control theory. Properties of the first two characteristics are well described in the literature. Properties of the Perron exponents are much less investigated. In this paper we show an example of two-dimensional discrete-time linear system with bounded coefficients for which the set of Perron exponents constitutes an interval.

Keywords: Time-varying systems, Linear systems
Abstract: This note proposes a new approach to computing the Kalman canonical decomposition of finite-dimensional linear periodic continuous-time systems by extending the Floquet theory. Controllable and observable subspaces are characterized by factorizing the monodromy matrix. Then, the conditions for the existence of several periodic Kalman canonical decompositions are extensively studied. The relations to the Floquet factorization, the Floquet-like factorization, and the period-specific realization are also discussed.

Keywords: Time-varying systems
Abstract: In this paper an analysis of the complexity involved in the implementation and synthesis of linear parameter-varying (LPV) controllers is presented. Its purpose is to provide guidance in the selection of a synthesis approach for practical LPV control problems and reveal directions for further research with respect to complexity issues in LPV control. Standard methods are classified into polytopic, linear fractional transformation and gridding-based techniques with an emphasis on output-feedback synthesis. Carried out as a convex optimization problem via finitely many linear matrix inequalities (LMIs) for both parameter-independent and parameter-dependent Lyapunov functions (PiDLF/PDLF), the complexity of LPV controller existence conditions is assessed in terms of the number of decision variables and total size of the LMI. The implementation complexity is assessed in terms of the number of arithmetic operations required to compute the parameter-varying state space matrices of the controller, as well as the memory requirements to store associated variables. The results are applied to the LPV controller synthesis for a three-degrees-of-freedom robotic manipulator and the charge control of a spark-ignited engine, for which multiple models, as well as associated synthesis results have been reported in the literature.

Keywords: Mechatronic systems, Robots manipulators, Modeling
Abstract: The paper deals with the new architecture of the 4 degrees of freedom manipulator for Non-Destructive Testing (NDT) of the pipe welds of complex geometries. The main contribution is to overcome the known disadvantages resulting from using standard robotic architectures (universal industrial or single purposes special manipulators) regarding e.g. space requirements or limited applicability. The direct and inverse kinematic is solved. The singularity configuration analysis of the manipulator is derived in the sense of finding implicit description of the varieties in the task space. It makes possible to easily deal with singular configurations during the end-effector trajectory planning. In order to use the proposed manipulator for NDT of pipe welds the trajectory generators are introduced. The circumferential, elbow, longitudinal and branch weld are taken into account. It is shown that the trajectory parametrization of the first three types of welds can be found easily. But for the branch weld there are some drawbacks regarding unnatural parametrization of the branch weld primitives (ellipses, Steinmetz solid). Therefore the new numerical algorithm is developed and used to ensure the requirements of the correct arc lengths are met. The algorithm is compared with its simplified version where equidistant spacing of the parametrization parameter is considered.

Keywords: Modeling, Autonomous robotic systems, Robotics technology
Abstract: Controlling a robotic system through natural language commands can provide great convenience for human users. Many researchers have investigated on high-level planning for natural language controlled robotic systems. Most of the methods designed the planning as open-loop processes and therefore cannot well handle unexpected events for realistic applications. In this paper, a closed-loop method is proposed for task-planning to overcome unexpected events occurred during implementation of assigned tasks. The designed system is modeled and theoretically proved to be able to stabilize the robotic system under unexpected events. Experimental results demonstrate effectiveness and advantages of the proposed method.

Keywords: Modeling, Identification and control methods, Flying robots
Abstract: This paper presents a solution to on-board trajectory tracking control of quadrotors. The proposed approach combines the standard hierarchical control paradigm that separates the control into low-level motor control, mid-level attitude dynamics control, and a high-level trajectory tracking with a model predictive control strategy. We use dynamic reduction of the attitude dynamics and dynamic extension of the thrust control along with feedback linearisation to obtain a linear system of relative degree three that models force controlled position and trajectory tracking for the quadrotor. Model predictive control is then used on the feedback equivalent system and its control outputs are transformed back into the inputs for the original system. The proposed structure leads to a low complexity model predictive control algorithm that is implemented in real-time on an embedded hardware. Experimental results on different position and trajectory tracking control are presented to illustrate the application of the derived linear system and controllers.

Keywords: Motion Control Systems, Vibration control, Auto-configuration
Abstract: This paper deals with motion control of an electrical drive with a compliant load. Particular problem of velocity PID control is studied due to extensive use of this control scheme in the industrial drives. The partial pole-placement method is used for derivation of feature-based parametrization of a set of stabilizing controllers which provide active vibration damping functionality. The achievable quality of control is evaluated by means of proper performance indices. Simulation experiments demonstrate the application of the proposed method.

Keywords: Modeling, Vibration control, Identification and control methods
Abstract: The frequency locking of optical cavities in the physics community has been traditionally performed using classical controllers. This approach works well as long as the system is not operating far from its linear region of operation. An optical cavity is however a highly nonlinear system and problems arise when it is affected by large disturbances. In this paper, we model the non-linearities arising in such an optical system and propose an extremum seeking controller to frequency lock the optical cavity. Simulation results are presented to validate the proposed scheme.

Keywords: Mechatronic systems
Abstract: In this paper, an improvement to Integral Force Feedback (IFF) damping control is proposed. The main limitation of Integral Force Feedback is that the maximum modal damping depends on the system’s parameters. Hence, for some system achievable damping is insignificant. The proposed improvement allows any arbitrary damping ratio to be achieved for a system by introducing a new feed-through term in the system. To achieve displacement tracking, one technique is to enclose the system in an integral feedback loop. However, the bandwidth is limited due to the effects of an additional pole in the damping loop. The proposed Structured PI controller is parameterised so that it contains a zero that cancel the additional pole. Experiment was conducted on a commercial objective lens positioner. The results show an exceptional tracking and damping performance and the system’s insensitivity to changes in resonance frequency. The maximum bandwidth achievable with a commercial PID controller is 26.1 Hz. In contrast, with the proposed method, the bandwidth is increased to 255 Hz.

Keywords: Decision support and control, Kinetic modeling and control of biological systems, Pharmacokinetics and drug delivery
Abstract: Patients with immune thrombocytopenic purpura (ITP), a disease characterized by abnormally low platelet count, are susceptible to excessive bleeding as a direct consequence. While the problem of low platelet count can be addressed fundamentally either by slowing down the rate of platelet destruction or by increasing platelet production, or both, one of the more effective means of treating ITP patients is to increase platelet production with romiplostim. However, current romiplostim treatment strategies tend to produce undesirable responses where platelet counts oscillate between dangerously low values and extremely high peaks, as a direct consequence of the complex nonlinear dynamics associated with platelet count regulation. The objective of this study is to determine the optimum dose profile of romiplostim for a specific ITP patient required to maintain a platelet count of 70×10^9/L. Using clinical data of the specific patient’s platelet count obtained in response to a series of subcutaneously applied doses of romiplostim, a standard pharmacokinetics/pharmacodynamics (PKPD) model was developed, validated, and analyzed to obtain insight into the patient’s physiological characteristics. The model was subsequently used to investigate the performance of three control strategies: fixed-dose open-loop control, optimally-tuned PI control, and model-based optimal open-loop control. The control strategies were implemented for weekly and bi-weekly treatment regimens. With both treatment frequencies, the fixed dose open-loop control strategy resulted in unacceptable sustained oscillating platelet count. PI control and model-based optimal open-loop control led to stable platelet count profiles after approximately 50 days but only for weekly injections. In summary, a stable platelet count is more likely to be achieved consistently in the specific patient with weekly treatments. Bi-weekly treatments are less effective because fundamental pharmaceutical characteristics of romiplostim make oscillations in platelet count unavoidable at this treatment frequency. The results show that model-based decisions determined using patient-specific mathematical models are potentially useful for designing better treatment regimens.

Keywords: Decision support and control, Kinetic modeling and control of biological systems
Abstract: The problem addressed is to construct a feedback regulator that stabilizes an equilibrium of the randomly forced nonlinear dynamic system, and synthesizes a required stochastic sensitivity of this equilibrium. The aim of this paper is to demonstrate how elaborated control theory can be used for the protection of the ecological systems against catastrophic shifts. In present paper, a stochastically forced predator-prey model with Allee effect is studied. A probabilistic mechanism of the noise-induced extinction of both species is shown. Using stochastic sensitivity functions technique, we construct confidence ellipses and estimate the threshold value of the intensity for noise generating a transition from the stable coexistence of both species to the extinction. For the stabilization of this population system, a method of the control for confidence ellipses is applied. It is shown that the regulator provides a low level of stochastic sensitivity and prevents unwanted ecological catastrophes.

Keywords: Model formulation, experiment design, Dynamics and control, Kinetic modeling and control of biological systems
Abstract: A healthy immune system exhibits robustness to disease where the robust stability and performance depends on the immune response mechanism. This paper presents an analysis of the immune system from the viewpoint of variable structure control. The immune response function is regarded as a switch. The influence of candidate personal immune response functions inspired by both experimental and mathematical work from immunology on capturing this crucial dynamics is reviewed. Motivated by this analysis and knowledge of variable structure control, a discontinuous switch which yields more realism and an improvement in the stability and robustness of the immune system has been designed and studied. In particular, surfaces which are functions of immune cell concentrations are identified. These form analogous functions to the well-known sliding mode surfaces which are known to represent a special class of variable structure control. These functions can be seen from the biological viewpoint as system properties which the healthy immune system appears to drive to zero, a well-known characteristic of the sliding mode paradigm.

Keywords: Physiological Model, Biomedical system modeling, simulation and visualization
Abstract: Subthreshold resonance has been observed in many excitatory/inhibitory neurons in the brain. It is suggested that such resonance phenomenon plays an important role in behavioral or perceptual functions in animals. However its mechanism and role remains unknown. Subthreshold resonance oscillation observed in a neuron suggests that a neuron has the frequency selectivity for various external inputs given to the brain. By advances in physiological experimental techniques, various voltage-dependent channels are thought to be involved in the generation of these resonance oscillations. Among these channels, a persistent sodium channel and a hyperpolarization-activated cation channel are said to mediate the generation of the subthreshold resonance oscillation observed in entorhinal cortex neurons and hippocampal pyramidal neurons. In this proposal, we examine how the external inputs affects the generation of action potentials at the neuron model with a persistent sodium channel and a hyperpolarization-activated cation channel parameters by computer simulation. Further more, the role of DC bias current input in the neuron model is also discussed.

Keywords: Kinetic modeling and control of biological systems, Cellular, metabolic, cardiovascular, pulmonary, neuro-systems, Pharmacokinetics and drug delivery
Abstract: In this paper, a relatively new computationally efficient technique, called model predictive static programming (MPSP), is extended further to incorporate a sequence of ‘impulse’ control inputs, which is subsequently used to propose an effective suboptimal automatic feedback radiotherapy strategy. A realistic two compartment kinetic model with oxygen effect is considered for computing the control sequence. Biologically effective dose constraints on early and late normal tissue are also considered. The proposed strategy essentially drives the radius of a tumor below the radius of a single cell, thereby driving the number of cancer cells to ‘zero’. Time interval between impulses is taken as 8 hrs and it is found that the tumor is driven to zero with approximately 25 impulses. Note that the MPSP algorithm is computationally quite efficient and it takes only 3-4 min in a regular desktop and MATLAB environment.

Keywords: Model formulation, experiment design, Rehabilitation engineering and healthcare delivery, Biomedical system modeling, simulation and visualization
Abstract: A non-linear model-based control system for the Human inspired Robotic Exoskeleton (HuREx) gait trainer was developed. The model used for the controller was derived using a hybrid PMA model and dynamic model of the device. The performance of the control system was compared against a closed-loop PID control system in simulation. The trajectory tracking results show that the model-based control system improved the speed and accuracy of the system as well as being robust against modeling errors and disturbances.

Keywords: Engine modelling and control, Modeling, supervision, control and diagnosis of automotive systems
Abstract: In the last decades the role of the engine control has became fundamental to improve fuel consumption and comply with the emissions standards. Particularly on automotive Diesel engines sophisticated control systems have been introduced, which are based on both large measurement data sets and complex controllers. Alternatives are needed to simplify and improve the Engine Management System (EMS). To this purpose the in-cylinder signal may be an effective measure to enhance EMS and its reliability, as well as to potentially reduce both costs and development time. The paper deals with the development of two techniques to estimate the Air-Fuel Ratio (AFR) and the in-cylinder mass from the in-cylinder pressure signal. These methodologies are intended to be implemented in real-time with the aim of replacing or working jointly with the existing air mass flow and lambda sensors. The techniques were validated vs. experimental data measured at the engine test bench on a Common Rail light-duty Diesel engine. The results show a good accuracy in predicting Air-Fuel Ratio and in-cylinder mass in a wide engine operating range.

Keywords: Engine modelling and control, Control architectures in automotive control
Abstract: A recently developed strategy for diesel engine management systems is modified to reduce the implementation complexity. The strategy calculates set points for engine management system controllable quantities with an aim to minimize fuel consumption for a given engine speed and requested torque profile, while keeping accumulated emissions below given limits. The strategy is based on the methodology for steady-state engine operation, but extended to handle transient effects in the engine caused by dynamics in the air system. The strategy leads to the parametrization of mappings with two, three and four input dimensions respectively. In this paper, a modification of the strategy is proposed such that the memory demanding multidimensional mappings can be approximated in an engine management system using only two-dimensional grid maps. The modified strategy has been evaluated using a complete diesel engine vehicle system model simulating the NEDC driving cycle. The performance of the modified strategy has been compared with the original performance of the strategy. It is demonstrated that the modification of the strategy has very little impact on resulting performance of a vehicle but requires considerably less memory for implementation.

Keywords: Modeling, supervision, control and diagnosis of automotive systems, Engine modelling and control
Abstract: A constraint tightened linear-time-varying MPC framework is proposed with applications in power tracking for variable and fixed speed generators. Current constraint tightening approaches are extended to allow for practical applications where future system representations are unknown. The resulting control structure is shown to be robustly feasible under given conditions. Knowledge about the geometry of system constraints is exploited to obtain a computationally efficient method of computing tightened sets online. A simulation study is presented demonstrating the ability of the controller to handle modelling error and demonstrate tracking of a commanded power profile.

Keywords: Nonlinear and optimal automotive control, Engine modelling and control
Abstract: Optimal control of a heavy duty diesel engine with EGR and VGT during transients is investigated. Minimum time and fuel optimal control problems are defined for transients from low to high output torque. A validated diesel engine model is used with minor changes in order to be suitable for the selected solver. The problem is solved for several feasible minimum EGR fractions and smoke-limiter values in order to provide comparisons. The optimization results show that the smoke-limiter has great effect on the transient duration while the required EGR fraction influences the control signals' shape. The fuel optimal control keeps the control actuators more closed than the time optimal, however both time and fuel optimal results become very similar when high EGR fractions and smoke-limiter values are required.

Keywords: Nonlinear and optimal automotive control
Abstract: Integrated Emission Management (IEM) is a supervisory control strategy that aims at minimizing the operational costs of diesel engines with an aftertreatment system, while satisfying emission constraints imposed by legislation. In previous work on IEM, a suboptimal real-time implementable solution was proposed, which was based on Pontryagin's Minimum Principle (PMP). In this paper, we compute the optimal solution using Dynamic Programming (DP). As the emission legislation imposes a terminal state constraint, standard DP algorithms are sensitive to numerical errors that appear close to the boundary of the backward reachable sets. To avoid these numerical errors, we propose Boundary Surface Dynamic Programming (BSDP), which is an extension to Boundary Line Dynamic Programming and uses an approximation of the backward reachable sets. We also make an approximation of the forward reachable sets to reduce the grid size over time. Using a simulation study of a cold-start World Harmonized Transient Cycle for a Euro VI engine, we show that BSDP results in the best approximation of the optimal cost, when compared to existing DP methods, and that the real-time implementable solution only deviates 0.16 [%] from the optimal cost obtained using BSDP.

Keywords: Automotive sensors and actuators, Adaptive and robust control of automotive systems, Engine modelling and control
Abstract: Engine block mounted accelerometers was used for estimating combustion phasing parameters for a heavy duty diesel engine. Several sensor locations were evaluated for its sensitivity to combustion related vibrations and mechanical noise.

One sensor-cylinder combination was selected for the evaluation of a simple algorithm to detect 10% and 50% burned mass fraction. The algorithm uses the accumulated accelerometer vibration energy as a base for extracting the 10% and 50% points respectively.

The results show that the angular positions for 10% and 50% burned mass fraction can be estimated with precisions of 1.5 and 3 CAD respectively. It is also concluded that the early part of the accelerometer signal has a significant influence from a mechanical noise source related to the start of injection.

Keywords: Mobile robots, Identification and control methods, Guidance navigation and control
Abstract: A leader-follower formation control scheme based on SRV-1 UGVs and an AR.DRONE 2.0 UAV as remote vision sensor is presented in this paper. The main advantage of the proposed strategy lies on the flexibility obtained from a flight remote sensor, as it makes possible to locate the agents at larger distances between them or to extend more easily the number of agents in the formation. A full description of the internal control designed for the UGVs and the UAV is presented, including the image processing procedure implemented to robustly measure the pose of the vehicles in the formation. Finally, experimental results using a triangular formation of three ground robots illustrates the effectiveness of the proposed control scheme.

Keywords: Mobile robots, Perception and sensing, Guidance navigation and control
Abstract: This paper proposes a vision-based minimum-time trajectory planning method for mobile robots, which takes into account kinematic constraints for linear/angular velocities and accelerations, as well as the visibility constraint. Different from existing methods, by means of homography-based pose estimation, the vision-based trajectory planning is formulated as a constrained optimal control problem in the scaled Euclidean space, which is solved by using the Gauss Pseudospectral Method (GPM). Specifically, the homography matrix is estimated and then decomposed to obtain the relative rotation angle and the scaled translation between the current pose and the desired one, which are expressed in the scaled Euclidean space. Then, kinematic constraints are taken into account in this space, while the visibility constraint is formulated by mapping the Euclidean homography matrix to the image space. To our best of knowledge, it is the first reported approach to solve the vision-based minimum-time trajectory planning problem for wheeled mobile robots, which can help improve the working efficiency in realistic visual servoing systems. Extensive simulation and experimental results compared with other methods are presented to demonstrate the effectiveness of the proposed approach.

Keywords: Guidance navigation and control, Robots manipulators
Abstract: This paper focus on dynamic visual seroving of a cable-driven soft robotic manipulator system. The soft robotic manipulator has no rigid structure. Based on Lagrange mechanics, kinetic energy, elastic potential energy and gravitational potential energy of each segment are analyzed, thus general dynamic equation of the soft robotic manipulator is obtained. On this basis, a depth-independent image Jacobian matrix is presented and an image-based visual servo controller is designed. Applied by adaptive algorithm, the controller could estimate unknown 3D feature positions online, and Lyapunov method is involved to prove the stability of the system. Experiments are conducted to demonstrate reasonableness and validity of dynamic model of the soft robotic manipulator and image-based adaptive visual servo controller.

Keywords: Guidance navigation and control, Robots manipulators, Autonomous robotic systems
Abstract: Traditional visual servoing systems do not deal with the topic of moving objects tracking. When these systems are employed to track a moving object, depending on the object velocity, visual features can go out of the image, causing the fail of the tracking task. This occurs specially when the object and the robot are both stopped and then the object starts the movement. In this work, we have employed a retina camera based on Address Event Representation (AER) in order to use events as input in the visual servoing system. The events launched by the camera indicate a pixel movement. Event visual information is processed only at the moment it occurs, reducing the response time of visual servoing systems when they are used to track moving objects.

Keywords: Perception and sensing, field robotics, Intelligent robotics
Abstract: Vision-based road detection plays a key role for Unmanned Ground Vehicles (UGVs) working in an unknown outdoor environment. The estimation of the vanishing point is a practical solution for general road detection using monocular vision, which however is not good enough for robust road detection in a campus environment due to the strong noises of texture orientations generated from roadside trees and buildings. In this paper, a novel system framework is proposed by combining the fast scene segmentation (FSS) and the rapid vanishing point detection. The proposed FSS algorithm can segment a single image into road and non-road regions based on the similarity analysis of color histogram, which can eliminate the inherent noises in the trees and buildings and improve the robustness of road detection effectively. Before voting for the vanishing point, we use Canny algorithm to extract the edges in the road region roughly segmented in FSS step. Since most of the strong texture orientations exist in the extracted edges, the computational complexity in the voting stage can be reduced significantly. Experimental results implemented on a real UGV platform show the validity and robustness of the proposed approach.

Keywords: Robots manipulators, Networked robotic system modeling and control, Design methodologies
Abstract: In this paper, we study the fixed-camera visual servoing consensus problem of multiple robotic manipulators with uncertain robotic dynamics, kinematics and camera parameters. Our control objective is to achieve image-space consensus without the measurements of visual velocity. The communication topology is assumed to be directed graphs containing a spanning tree. A novel decentralized image-space position observer with online parameter updating is presented to avoid the reliance on visual velocity and to handle the uncertain robotic kinematics and camera parameters. Based on the observed visual information, we perform the distributed adaptive controller design in a cascade framework. The asymptotic convergence of consensus error is proved by use of Lyapunov analysis tool and input-output stability analysis tool. Finally, simulations with networked robotic manipulators are performed to validate the effectiveness of the proposed strategy.

Keywords: Distributed control and estimation, Multi-agent systems
Abstract: High-voltage direct current (HVDC) is a commonly used technology for long-distance power transmission, due to its low resistive losses and low costs. In this paper, a novel distributed controller for multi-terminal HVDC (MTDC) systems is proposed. Under certain conditions on the controller gains, it is shown to stabilize the MTDC system. The controller is shown to always keep the voltages close to the nominal voltage, while assuring that the injected power is shared fairly among the converters. The theoretical results are validated by simulations, where the affect of communication time-delays is also studied.

Keywords: Distributed control and estimation, Control of networks
Abstract: This paper presents synthesis results for distributed controllers for interconnected linear time-invariant systems. The setting of the paper is in discrete time. A parameterization of the closed-loop system is used for interconnected systems and distributed static state feedback controllers with an interconnection structure that can be chosen arbitrary in the design phase. Based on conditions for closed-loop stability using centralized static state feedback, computationally tractable synthesis procedures are derived that yield a distributed controller. The synthesis procedures involve convex optimization problems in the form of linear matrix inequalities (LMIs) which are solved in a centralized way. Suggestions are made to reduce the number of independent variables in the problems. An algorithm is presented that uses these synthesis procedures to find a controller with a distributed structure or eliminates candidate controller structures using heuristics. A complexity analysis for the synthesis procedures is incorporated. The synthesis algorithm is illustrated on examples of electric power systems, proving feasibility of the synthesis procedures for real-life applications.

Keywords: Distributed control and estimation, Control over networks
Abstract: An industrial case study in the form of a large-scale hydraulic network underlying a district heating system is considered. A distributed control is developed that minimizes the aggregated electrical energy consumption of the pumps in the network without violating the control demands. The algorithm is distributed in the sense that all calculations are implemented where the necessary information is available, including both parameters and measurements. A communication network between the pumps is implemented for global optimization. The local implementation of the algorithm means that the system becomes a Plug & Play control system as most commissioning can be done during the manufacture of the pumps. Only information on the graph-structure of the hydraulic network is needed during installation.

Keywords: Distributed control and estimation, Networked embedded control systems, Cooperative systems
Abstract: We consider distributed methods for detection of cyber attacks and faults in power networks. The approach is based on grouping of buses in the system, and for each group, we design filters for detection and isolation of faults applied to the buses within the group. The scheme is distributed in the sense that it uses only locally available data such as the generated power, loads, and power flows. Specifically, a fault detection method based on a geometric approach is applied to two different settings. Depending on the availability of phasor measurement units, we develop indirect and direct methods and compare their characteristics in performance and computation. A numerical example is provided to illustrate the results.

Keywords: Smart grids, Control of renewable energy resources
Abstract: Abstract: Control strategies of distributed generation (DG) are investigated for different combination of DG and storage units in a microgrid. This paper develops a detailed photovoltaic (PV) array model with maximum power point tracking (MPPT) control, and presents real and reactive power (PQ) control and droop control for DG system for microgrid operation. In grid-connected mode, PQ control is developed by controlling the active and reactive power output of DGs in accordance with assigned references. In islanded mode, DGs are controlled by droop control. Droop control implements power reallocation between DGs based on predefined droop characteristics whenever load changes or the microgrid is connected/disconnected to the grid, while the microgrid voltage and frequency is maintained at appropriate levels. This paper presents results from a test microgrid system consisting of a voltage source converter (VSC) interfacing with a DG, a PV array with MPPT, and changeable loads. The control strategies are tested via two scenarios: the first one is to switch between grid-connected mode and islanded mode and the second one is to change loads in islanded mode. Through voltage, frequency, and power characteristics in the simulation under such two scenarios, the proposed control strategies can be demonstrated to work properly and effectively.

Keywords: Intelligent control of power systems, Control system design, Application of power electronics
Abstract: This paper proposes a control scheme based on discrete-time block control technique using sliding modes, for a system composed of a three-phase rotatory induction motor when includes a gear.The goal is tracking position trajectory. A recurrent high order neural network (RHONN) is used to identify the system, which is trained with an Extended Kalman Filter (EKF) algorithm. Its performance is illustrated via simulations.

Keywords: Control of renewable energy resources
Abstract: For renewable energy generation and fault tolerant applications, a multiphase PMSG seems to be an interesting solution. In this paper, a fault-tolerant Control of a 5-phase PMSG Non-Sinusoidal EMF for Marine Current Turbine applications under fault operation is proposed. In order to maximize the average torque, to minimize the copper losses and to reduce the torque ripples all harmonics of EMF are exploited under fault opearation. As the optimal current references in the Concordia’s frame are not constants, the chosen regulator must have a high dynamic performance even if the machine speed varies. It must ensure reference tracking without affecting the signal magnitudes or introduce phase shift. Hence, a Fractional controller is investigated to control the non-constant current references in the Concordia’s frame under fault operation.

Keywords: Control system design, Application of power electronics, Instrumentation and control systems
Abstract: This paper derives the algorithm of integral plus finite control set (FCS) -predictive control for AC motor drives. In the paper, it is shown that the original FCS-predictive control algorithm in the d−q reference frame is equivalent to a dead-beat control system in the presence of constraints, where the closed-loop system is approximated by a unit time delay. Without integral action, the original FCS-predictive control system contains steady-state errors in both d-axis and q-axis currents, hence compromising closed-loop performance. Using an integral control in a cascaded structure to the original FCS-predictive control, a simple algorithm is proposed to eliminate the steady-state errors of the current control system and improve its closed-loop performance. The sampling interval of the current control system is used as a performance tuning parameter for reduction of current variations. Furthermore, the algorithm is expressed in a velocity form for convenience in implementation using a digital signal processor. Experimental results are used to show the successful design and implementation of the integral finite control set (I-FCS) predictive control.

Keywords: Application of power electronics, Optimal operation and control of power systems, Control system design
Abstract: This study concerns the power supply in parallel of 2 Permanent Magnet Synchronous Machines (PMSM) with the same inverter, in order to reduce the mass of embedded systems. This operation requires information from the two machines to ensure the stability of the system. Today several solutions address this objective, but about to improve energy efficiency it is important to develop new algorithms. The classical approach is based on a master-slave procedure where the current is controlled only in a master machine and the slave machine is simply connected in parallel. This solution guarantees stability but losses are not minimal. Predictive control applied to this configuration helps to ensure the stability of the system and gives the possibility to reduce Joule losses. In this way 3 predictive control laws are proposed by setting appropriated criteria. The first law uses the concept of master-slave machine, the second is defined with a criterion taking into account the two machines and the third uses the concept of virtual vectors which leads to an implementation of Space-Vector Pulse Width Modulation (SV-PWM. Results are compared in terms of dynamic performances and losses. The algorithms are tested on an experimental low-power test-bench.

Keywords: Modeling and simulation of power systems, Control system design, Application of power electronics
Abstract: This paper presents research motivated by industrial demand for special novel high voltage traction drive topology devoted to minimization of traction transformer weight against classical locomotives operates on both 25kV/50Hz and 15kV/16⅔Hz trolley voltages. The traction converter topology consists of: two single phase matrix converter + medium frequency transformer (MFT) + single phase voltage-source active rectifier. Measured results of steady state and selected transient states verifying of proper function of control algorithms. The new control strategy of traction converter based on zero vectors insertion has been proposed to smoothing taken trolley current.

Keywords: Constrained control, control of fluid flows and fluids-structures interactions, Real-time control
Abstract: In the application of an autonomous underwater vehicle a critical requirement is to keep the level of the actuation signals within operational limits to avoid, for example, actuator nonlinearities and reduce peak power consumption. The most common approach to this problem for AUVs that have been deployed is, if required, to trade-off performance in order to keep the actuation signals and power required within the operational limits. This paper addresses depth control of an AUV using model predictive control with constraints on the both the amplitude and rate of change of the entries in the control vector. The model predictive control algorithm is designed by solving a quadratic programming problem in real-time when implemented on an AUV prototype. Experimental test results for depth control are also given and demonstrate that physically relevant constraints on the thrust and actuation power, critical factors for the use of these vehicles, can be achieved. Moreover, there is agreement between the control action used and the underlying physics of a body moving in water.

Keywords: Constrained control, Convex optimization, Real-time control
Abstract: In this work, we apply the fast alternating minimization algorithm (FAMA) to model predictive control (MPC) problems with polytopic and second-order cone constraints. We present a splitting strategy, which speeds up FAMA by reducing each iteration to simple operations. We show that FAMA provides not only good performance for solving MPC problems when compared to other alternating direction methods, but also superior theoretical properties. Specifically, we derive complexity bounds on the number of iterations for both dual and primal variables, which are of particular relevance in the context of real-time MPC to bound the required online computation time. For MPC problems with polyhedral and ellipsoidal constraints, an off-line pre-conditioning method is presented to further improve the convergence speed of FAMA by decreasing the complexity upper-bounds and enlarging the step-size of the algorithm. Finally, we demonstrate the performance of FAMA compared to other alternating direction methods using a quadroter example.

Keywords: Constrained control, Industrial applications of optimal control
Abstract: This paper investigates model predictive control (MPC) of a sea wave energy converter(WEC). A novel objective function is adopted in the MPC design, which brings obvious benefits: First, the quadratic program (QP) derived from this objective function can be easily convexified, which facilitates the employment of existing efficient optimization algorithms. Second, this novel design can trade off the energy extraction, the energy consumed by the actuator and safe operation. The effectiveness of this MPC strategy is demonstrated by numerical simulations.

Keywords: Control of constrained systems, Controller constraints and structure, Constrained control
Abstract: We first recall results on the boundary of the so-called admissible set for state and input constrained nonlinear systems, namely that the boundary is made up of two parts: one included in the state constraints and its complement called the barrier, made of integral curves that satisfy a minimum-like principle. Then we define the notions of barrier stopping points by intersection and by self-intersection. We then prove that all regular intersection points of the integral curves running along the barrier are barrier stopping points. Then we present, on systems of two and three dimensions, examples where barriers with stopping points occur.

Keywords: Control of constrained systems, Nonlinear predictive control, Robust controller synthesis
Abstract: Optimal robust MPC for constrained linear systems that are subject to additive uncertainty requires a closed loop optimization with computation that rises exponentially with the prediction horizon, N. This was overcome by parameterized tube MPC (PTMPC) that used a triangular separable prediction strategy in which predictions are decomposed into a nominal and N-1 partial prediction sequences, each concerned with the compensation of a particular future disturbance. Beyond the first N steps, the PTMPC control law can be taken to be a static state feedback. Use of N-1 partial prediction sequences causes the number of free variables and constraints to rise quadratically with N. To reduce this to linear, this paper proposes a modification of PTMPC that uses a single partial prediction sequence that is used in connection with all particular future disturbances but allows the compensatory effect of the partial prediction sequences to extend beyond the first N predictions. The prediction structure is of a triangular striped nature and extends over an infinite horizon. As illustrated by a simulation example, the resulting scheme can outperform PTMPC in terms of the size of the domain of attraction and affords a reduction in computation which allows for the use of longer horizon (for the same computational demand).

Keywords: Nonlinear predictive control, Control under communication constraints (nonliearity), Control of interconnected systems
Abstract: A method to ensure recursive feasibility and asymptotic stability for a distributed model predictive control scheme in case of temporary communication failure is presented. The considered networks are made up of dynamically uncoupled nonlinear systems, coupled through constraints and objectives. The proposed method substitutes affected coupling constraints in such a way that the scheme can not become infeasible, while a decrease in the cost is guaranteed.

Keywords: Vehicle dynamic systems, Nonlinear and optimal automotive control, Electric and solar vehicles
Abstract: In this paper, we propose a torque vectoring controller for an electric vehicle with two electric machines which drive the front wheels independently. The torque vectoring system includes a vehicle dynamics controller and a motor torque and wheel slip limiter. The vehicle dynamics controller is designed as a polytopic, linear parameter-varying (LPV) gain-scheduled controller. The LPV controller tracks the longitudinal velocity and the yaw rate of the vehicle. A torque and slip limiter (TSL) is developed to deal with physical saturation of the electric motors and wheel slip limitations which are related to the traction of the tires. The TSL deals with actuator limitations and prevents the wheel from spinning or blocking. Following the design, the controller is converted into fixed-point representation and is implemented on an automotive-qualified micro controller. As part of the European project eFuture, test drives are performed to obtain experimental data. For comparison the automotive prototype is driven with both, equal torque and the designed torque vectoring controller. The differences between the two approaches are illustrated with experimental results for a double lane change maneuver.

Keywords: Vehicle dynamic systems, Nonlinear and optimal automotive control, Electric and solar vehicles
Abstract: This paper presents the torque vectoring control concept for a vehicle with two powerful wheel individual electric drives at the rear axle. The direct yaw moment control which is enabled by the torque difference offers the potential for shaping the vehicle’s yaw dynamics in a considerable range. The control concept introduced here is primarily oriented at the practical target of demonstrating the potential of the prototype vehicle’s innovative rear axle. Nevertheless, together with the tools presented here it is conveniently adaptable to any vehicle data. The focus is on yaw dynamics control. A reference yaw rate is determined by combining conveniently tunable linear dynamics with a nonlinear steady state gain, the latter in order to establish a desired self-steering behavior. In order to reshape the yaw response after steering inputs an inverse single track model is used as a significant part of the applied feed-forward control. Moreover, the same inverse model is employed by the optional yaw rate feedback control which is based on the inverse disturbance observer scheme. Both the effectiveness of the control concept and the practical ease of control parameter tuning were validated in driving experiments.

Keywords: Adaptive and robust control of automotive systems, Control architectures in automotive control, Vehicle dynamic systems
Abstract: Robust design of an torque vectoring control based on an inverse disturbance observer architecture for a rear wheel driven vehicle with electric single wheel drives is presented within this contribution. To ensure robust control parameters, specifications for eigenvalues (Gamma-stability) and bounds on weighted sensitivity and complementary sensitivity functions in frequency domain (B-stability) are formulated and transfered into parameter space. Simulation results show the effectiveness of the chosen controller design and parameter setup.

Keywords: Vehicle dynamic systems, Automotive system identification and modelling, Automotive sensors and actuators
Abstract: The knowledge of the forces acting between tire and road is crucial for modern vehicle control systems. Typically, such forces are estimated on the basis of indirect measurements and rely on complex mathematical models. Sensors embedded in the tire (e.g. accelerometers) offer the possibility of directly reaching the area of the tire-road interaction, and of delivering signals during the wheel rotation. Such signals reflect the tire state and the force exchanged between tire and road.

In this paper an innovative approach to estimate such forces is presented. It exploits the signal delivered during the wheel rotation by an accelerometer glued to the inner side of the tire tread. The approach consists in processing the signal by means of the Principal Component Analysis (PCA). The results of experimental activities employing a real vehicle provide evidence to support the feasibility and the soundness of the proposed approach.

Keywords: Nonlinear and optimal automotive control, Control architectures in automotive control, Adaptive and robust control of automotive systems
Abstract: We propose a torque overlay based robust steering wheel angle control of electric power steering for lateral control using bakcstepping design. The external disturbances, system function, and input gain uncertainty are regarded as a disturbance. An augmented observer is designed to estimate full state and the disturbance. A nonlinear damping controller is developed via backstepping to suppress a position tracking error using input-to-state stability (ISS) property. The proposed method uses only steering wheel angle feedback and nominal value of the input gain without the system modeling. The proposed method is simple to implement in real-time control and robust to the parameter uncertainties and the external disturbance. Since the proposed method is designed based on torque overlay as add-on type, it can be integrated with the conventional electric power steering system (EPS) facilitating driver's intervention. The performance of the proposed method was validated via experiments.