Plenary Lectures

A gray-box model is one that has a known structure (generally constrained to a strict subset of the class of models it is drawn from) but has unknown parameters. Such models typically embody or reflect the underlying physical or mechanistic understanding we have about the system, as well as structural features such as the delineation of subsystems and their interconnections. The unknown parameters in the gray-box model then become the focus of our system identification efforts. In a variety of application domains, ranging from biology and medicine to power systems, the gray-box models that practitioners accept – as plausible representations of the reality they deal with every day – have been built up over decades of study, and are large, detailed and complex. In addition to being difficult to simulate or compute or design with, a significant feature of these models is the uncertainty associated with many or most of the parameters in the model. The data that one collects from the associated system is rarely rich enough to allow reliable identification of all these parameters, yet there are good reasons to not be satisfied with direct black-box identification of a reduced-order model. The challenge then is to develop meaningful reduced-order gray-box models that reflect the detailed, hard-won knowledge one has about the system, while being better suited to identification and simulation and control design than the original large model. Practitioners generally seem to have an intuitive understanding of what aspects of the original model structure, and which variables and parameters, should be retained in a physically or mechanistically meaningful reduced-order model for whatever aspect of the system behavior they are dealing with at a particular time. Can we capture and perhaps improve on what they are doing when they develop their (often informal) reduced models?

This talk will illustrate and elaborate on the above themes. Examples will be presented of approaches and tools that might be used to explore and expose structure in a detailed gray-box model, to guide gray-box reduction.

George Verghese received his B. Tech. from the Indian Institute of Technology, Madras, in 1974, his M.S. from the State University of New York at Stony Brook in 1975, and his Ph.D. from Stanford University in 1979, all in electrical engineering. Since 1979 he has been at the Massachusetts Institute of Technology, where he is Professor of Electrical Engineering in the Department of Electrical Engineering and Computer Science, and a member of the Laboratory for Electromagnetic and Electronic Systems. His research interests are in modeling, estimation and control, particularly for switched or networked systems, and primarily in applications related to power systems and to biology and biomedicine. He is co-author (with J.G. Kassakian and M.F. Schlecht) of “Principles of Power Electronics” (Addison-Wesley, 1991), and co-editor (with S. Banerjee) of “Nonlinear Phenomena in Power Electronics” (IEEE Press, 2001.) Dr. Verghese is a Fellow of the IEEE.

Networked embedded sensing and control systems are increasingly becoming ubiquitous in applications from manufacturing, chemical processes and autonomous robotic space, air and ground vehicles, to medicine and biology. They offer significant advantages, but present serious challenges to information processing, communication and decision-making. This area, called cyber-physical systems, which has been brought to the forefront primarily because of advances in technology that make it possible to place computational intelligence out of the control room and in the field, is the latest challenge in systems and control, where our quest for higher degrees of autonomy has brought us, over the centuries, from the ancient water clock to autonomous spacecrafts. Our quest for autonomy leads to consideration of increasingly complex systems with ever more demanding performance specifications, and to mathematical representations beyond time-driven continuous linear and nonlinear systems, to event-driven and to hybrid systems; and to interdisciplinary research in areas at the intersection of control, computer science, networking, driven by application needs in physics, chemistry, biology, finance.

After an introduction to some of the main research and education issues we need to address and a brief description of lessons learned in hybrid systems research, we shall discuss recent methodologies we are currently working on to meet stability and performance specifications in networked control systems, which use passivity, model-based control and intermittent feedback control.

Panos Antsaklis is the H. Clifford and Evelyn A. Brosey Professor of Electrical Engineering and Concurrent Professor of Computer Science and Engineering at the University of Notre Dame. He served as the Director of the Center for Applied Mathematics of the University of Notre Dame from 1999 to 2005.  He is a graduate of the National Technical University of Athens (NTUA), Greece, and holds MS and PhD degrees from Brown University.

His research addresses problems of control and automation and examines ways to design engineering systems that will exhibit high degree of autonomy in performing useful tasks. His recent research focuses on networked embedded systems and addresses problems in the interdisciplinary research area of control, computing and communication networks, and on hybrid and discrete event dynamical systems.

After initial work on the Polynomial Matrix Descriptions and characterization of all stabilizing controllers, in the late 80s his group helped establish Autonomous Intelligent Control in the mainstream control research community; in the early 90s, introduced Supervisory Control of Discrete Event Systems (DES) using Petri nets; in the mid-90s helped establish Hybrid Control Systems and influenced its research directions. Since 2000 his group is involved in Sensor and Control Networks, Networked Control Systems, in the Distributed Control of Multi-agent Systems and in the analysis and synthesis of ubiquitous and complex Cyber-Physical Systems (CPS). There is a strong common thread that permeates all these research areas that led to the study of these research topics, namely the Quest for Autonomy in complex systems, wanting to build Intelligent, High Autonomy control systems.

He has authored a number of publications in journals, conference proceedings and books, and he has edited six books on Intelligent Autonomous Control, Hybrid Systems and on Networked Embedded Control Systems.  In addition, he has co-authored the research monographs "Supervisory Control of Discrete Event Systems Using Petri Nets" (Kluwer Academic 1998, with J. Moody) and "Supervisory Control of Concurrent Systems: A Petri Net Structural Approach" (Birkhauser 2006, with M.V. Iordache) and the graduate textbooks "Linear Systems" and “A Linear Systems Primer” (Birkhauser 2007, with A.N. Michel)

He has been Guest Editor of special issues in IEEE Transactions of Automatic Control (April 98 & Sept 04) and the Proceedings of IEEE (July 00 & Jan 07) on Hybrid and on Networked Control Systems. He serves in the editorial boards of several journals, and he currently serves as AEAL of the IEEE Trans. Automatic Control. He has served as program chair and general chair of major systems and control conferences including the Conference on Decision and Control (PC of 1991 CDC and GC of 1995 CDC) and he was the 1997 President of the IEEE Control Systems Society (CSS). He has been plenary and keynote speaker in a number of conferences and research workshops.

He currently serves as the president of the Mediterranean Control Association. He serves in the Scientific Advisory Board for the Max-Planck-Institut fur Dynamik Komplexer Technischer Systeme, Magdeburg, Germany. He was member of the subcommittee on Networking and Information Technology of the President’s Council of Advisors for Science and Technology (PCAST), which advises the President of the United States on Science and Technology federal policy issues regarding technology, scientific research priorities, and math and science education. He is an IEEE Fellow for his contributions to the theory of feedback stabilization and control of linear multivariable systems, was a Distinguished Lecturer of the IEEE Control Systems Society, is a recipient of the IEEE Distinguished Member Award of the Control Systems Society, and an IEEE Third Millennium Medal recipient. He was the 2006 recipient of the Brown Engineering Alumni Medal of Brown University, Providence, Rhode Island. He has been elected to be the new Editor-in-Chief of the IEEE Transactions of Automatic Control starting in 2009.

Most individuals form their opinions about the quality of products, social trends and political issues via their interactions in social and economic networks. While the role of social networks as a conduit for information is as old as humanity, recent social and technological developments, such as Facebook, Blogs and Tweeter, have added further to the complexity of network interactions. Despite the ubiquity of social networks and their importance in communication, we know relatively little about how opinions form and information is transmitted in such networks. For example, does a large social network of individuals holding disperse information aggregate it efficiently? Can falsehoods, misinformation and rumors spread over networks? Do social networks, empowered by our modern communication means, support the wisdom of crowds or their ignorance? Systematic analysis of these questions necessitates a combination of tools and insights from game theory, the study of multiagent systems, and control theory. Game theory is central for studying both the selfish decisions and actions of individuals and the information that they reveal or communicate. Control theory is essential for a holistic study of networks and developing the tools for optimization over networks.

In this talk, I report recent work on combining game theoretic and control theoretic approaches to the analysis of social learning over networks.

Asu Ozdaglar received the B.S. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1996, and the S.M. and the Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, in 1998 and 2003, respectively. Since 2003, she has been a member of the faculty of the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology (MIT), where she is currently the Class of 1943 Career Development Associate Professor. She is also a member of the Laboratory for Information and Decision Systems (LIDS) and the Operations Research Center (ORC) at MIT.

Her research interests include optimization theory (with emphasis on nonlinear programming, convex analysis and nonconvex optimization), game theory, network economics, distributed optimization methods, and network optimization and control. She is the co-author (with Dimitri P. Bertsekas and Angelia Nedic) of the book entitled “Convex Analysis and Optimization” (Athena Scientific, 2003). She is the recipient of a Microsoft fellowship, the MIT Graduate Student Council Teaching award, the NSF Career award, and the 2008 Donald P. Eckman award of the American Automatic Control Council.