Plenary Lectures

The aerospace industry poses unique challenges to the controls engineer. This presentation will use the Lockheed Martin Orion Multi-Purpose Crew Vehicle (MPCV) as a case study, examining the disparate flight regimes and vehicle configurations to highlight some of the more interesting facets of atmospheric and orbital controls. Time-varying nonlinear external disturbances, step changes in the plant's attributes, balance of forward gain control authority with integrated system precision, coupled effector/disturbance interactions, outer-loop guidance considerations, and the effect of pilot-in-the-loop flight on controls system development will all be explored. Along the way, the presentation will identify how balancing these considerations played into the integrated Orion controls system design.

Biography

John Nelson was appointed as Director, SSC Chief Engineer in February 2011. Prior to that, he served as Director of the Guidance, Navigation, and Control Subsystems organization, and before that as the Technical Director for the Missile Defense Systems Line of Business. John has over 27 years of technical and leadership experience in guidance, navigation, and control as well as systems engineering. He has supported a variety of programs including Special Programs, Airborne Laser, GPS III, and Hubble Space Telescope, in addition to service in functional leadership roles. John earned a bachelor's degree in Aeronautical Engineering from the University of Illinois and master's and engineer's degrees in Aeronautics/Astronautics from Stanford University.

Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease affecting approximately 3 million individuals in the US, with associated annual healthcare costs estimated to be $15 billion. Current treatment requires either multiple daily insulin injections or continuous subcutaneous (SC) insulin infusion (CSII) delivered via an insulin infusion pump. Both treatment modes necessitate frequent blood glucose measurements to determine the daily insulin requirements for maintaining near-normal blood glucose levels.

More than 30 years ago, the idea of an artificial endocrine pancreas for patients with type 1 diabetes mellitus (T1DM) was envisioned. The closed-loop concept consisted of an insulin syringe, a blood glucose analyzer, and a transmitter. In the ensuing years, a number of theoretical research studies were performed with numerical simulations to demonstrate the relevance of advanced control design to the artificial pancreas, with delivery algorithms ranging from simple PID, to H-infinity, to model predictive control. With the advent of continuous glucose sensing, which reports interstitial glucose concentrations approximately every minute, and the development of hardware and algorithms to communicate with and control insulin pumps, the vision of closed-loop control of blood glucose is approaching a reality.

In the last 8 years, our research group has been working with medical doctors on clinical investigations of control algorithms for the artificial pancreas. In this talk, I will outline the difficulties inherent in controlling physiological variables, the challenges with regulatory approval of such devices, and will describe a number of algorithms we have tested in clinical experiments for feedback control of the artificial pancreas, based on model predictive control.

This presentation is based on work coauthored with Eyal Dassau, Rebecca Harvey, Matt Percival, Benny Grosman, Howard Zisser, Dale Seborg, and Lois Jovanovic.

Biography

Frank Doyle holds the Duncan and Suzanne Mellichamp Chair in Process Control in the Department of Chemical Engineering, as well as appointments in the Electrical Engineering Department, and the Biomolecular Science and Engineering Program at UC, Santa Barbara. He is the Director of the UCSB/MIT/Caltech Institute for Collaborative Biotechnologies, and is the Associate Dean for Research in the College of Engineering. He received a B.S.E. degree from Princeton, C.P.G.S. from Cambridge, and Ph.D. from Caltech, all in Chemical Engineering. Prior to his appointment at UCSB, he has held faculty appointments at Purdue University and the University of Delaware, and held visiting positions at DuPont, Weyerhaeuser, and Stuttgart University. He has been recognized as a Fellow of multiple professional organizations including: the IEEE, IFAC, AIMBE, and the AAAS. He served as the editor-in-chief of the IEEE Transactions on Control Systems Technology from 2004-2009, and is currently the Vice President for Publications in the Control System Society. In 2005, he was awarded the Computing in Chemical Engineering Award from the AIChE for his innovative work in systems biology. His research interests are in systems biology, network science, modeling and analysis of circadian rhythms, drug delivery for diabetes, model-based control, and control of particulate processes.

The past decade has seen tremendous advances in the fields of Systems and Synthetic Biology to the point that de novo creation of simple biomolecular networks, or “circuits”, in living organisms to control their behavior has become a reality. A near future is envisioned in which re-engineered bacteria will turn waste into energy and kill cancer cells in ill patients. To meet this vision, one key challenge must be tackled, namely designing biomolecular networks that can realize substantially more complex functionalities than those currently available.

 A promising approach to analyzing or designing complex networks is to modularly connect simple components whose behavior can be isolated from that of the surrounding modules. The assumption underlying this approach is that the behavior of a component does not change upon interconnection. This is often taken for granted in fields such as electrical engineering, in which insulating amplifiers enforce modular behavior by suppressing impedance effects. This triggers the fundamental question of whether a modular approach is viable in biomolecular circuits. Here, we address this research question and illustrate how, just as in many mechanical, hydraulic, and electrical systems, impedance-like effects are found in biomolecular systems. These effects, which we call retroactivity, dramatically alter the behavior of a component upon interconnection. By merging disturbance rejection and singular perturbation techniques, we provide an approach that exploits the structure of biomolecular networks to design insulating amplifiers. We provide the first experimental demonstration of our theory on a reconstituted protein modification cycle extracted from bacterial signal transduction and show that the cycle can be tuned to function as an insulating amplifier. This also reveals another reason why protein modification cycles, such as phosphorylation, are ubiquitous in natural signal transduction networks; they can function as insulating amplifiers and enforce unidirectional signal propagation. This is certainly desirable in any (human-made or natural) signal transmission system.

Biography

Domitilla Del Vecchio received the Ph. D. degree in Control and Dynamical Systems from the California Institute of Technology, Pasadena, and the Laurea degree in Electrical Engineering from the University of Rome at Tor Vergata in 2005 and 1999, respectively. From 2006 to 2010, she was an Assistant Professor in the Department of Electrical Engineering and Computer Science and in the Center for Computational Medicine and Bioinformatics at the University of Michigan, Ann Arbor. In 2010, she joined the Department of Mechanical Engineering and the Laboratory for Information and Decision Systems (LIDS) at the Massachusetts Institute of Technology (MIT), where she is currently the W. M. Keck Career Development Assistant Professor in Biomedical Engineering.  She is a recipient of the Donald P. Eckman Award from the American Automatic Control Council (2010), the NSF Career Award (2007), the Crosby Award, University of Michigan (2007), the American Control Conference Best Student Paper Award (2004), and the Bank of Italy Fellowship (2000). Her research interests include analysis and control of nonlinear and hybrid dynamical systems and the analysis and design of bio-molecular networks.

The legacy electrical power grid has been quietly evolving from power generation and distribution controlled by a balanced suite of analog power electronics to a hybrid and distributed enterprise of power balancing analytics and information systems services known as the Smart Grid. This advanced perspective of the power grid goes far beyond what the Internet revolution has done with national scale information systems. The smart grid moves to integrate highly sophisticated control systems that balance voltage, phase and reactive power with a layer of informatics that orchestrates dynamic pricing, economically dispatched demand response, distributed generation and collaborative grid operations. The smart grid vision presents an interesting perspective of a distributed and hybrid control system. It offers opportunity to examine control, not only from an algorithmic sense, but also requires examining the architecture of control as it exists at the enterprise level in large-scale systems of systems. This presentation examines the architecture and control taxonomy in the Smart Grid. It discusses smart grid goals and control heuristics and examines the holistic focus necessary and technical innovation required as analog-centric electric power balancing merges with digital-centric information processing and semantic tagging.

This presentation is based on a paper authored by Ray Piasecki titled Control Heuristics of Intelligent Microgrids.

Biography

Mr. Piasecki began his career as a member of the research staff in the information systems laboratory at General Dynamics in 1982 and worked there for 28 years ultimately achieving the position of engineering fellow. In that capacity, Ray investigated information and semantic modeling and its application in advancing search and discovery of structured and unstructured data in heteorgenous and distributed systems. This research path evolved from data discovery to also include applications of distributed and collaborative control in agent based systems and the application of semantic models to discover not only data but also service behaviors in distributed systems and software components. The technology and applications developed from these studies were applied in several national intelligence programs that developed large scale distributed geospatial data centers, intelligent and cooperative agent systems orchestrating collaborative data processing and control functions and enterprise analytics services performing entity and concept discovery and semantic link analysis. Most recently, Ray has joined GE Energy and performs consulting on smart grid projects for utilities which are applying information systems constructs to the electrical grid and creating intelligent and automated power systems services.

Mr. Piasecki has contributed to various working groups and technical committee’s including OMG, IEEE, KM.Gov, JavaSoft, CIMug, GridWise Architecture Council, OpenSG and NIST interoperability PAP’s. Mr. Piasecki has also written papers for and served as speaker at numerous industry technical conferences including AFEI International Conference on Enterprise Transformation, Federal CIO Semantic Interoperability Community of Practice, OMG Object World, Technology of Object Oriented Languages and Systems (Tools), SemTech International Semantic Technology Conference, GridInterop and World Energy Congress.