CDC

Switched Systems with Positivity Constrations

Lecture Details

Date:

2010

Location Information:

IEEE 2010 CDC Semi-Plenary Lecture

Author:

Maria Elena Valcher

Author Bio:

Maria Elena Valcher received the Master Degree in Electronic Engineering (1991) and the Ph.D. (1995) from the University of Padova, Italy. Since January 2005, she is Full Professor of Control Theory at the University of Padova.

She is author/co-author of more than 50 papers appeared on international journals, 70 conference papers, 2 text-books and 14 book chapters. Her research interests include multidimensional systems theory, polynomial matrix theory, behavior theory, convolutional coding, fault detection, delay-differential systems, positive systems and positive switched systems.

She has been involved in the Organizing Committees and in the Program Committees of several conferences. She is the Program Chair of the CDC 2012. She was in the Editorial Board of the IEEE Transactions on Automatic Control (1999-2002) and she is currently in the Editorial Boards of Automatica (2006-today), Multidimensional Systems and Signal Processing (2004-today) and Systems and Control Letters (2004-today).

She was and still is a Member of the CSS BoG (Appointed 2003; Elected 2004-2006; Elected 2010-2012). She was the CSS Vice President Member Activities (2006-2007) and she is Vice President Conference Activities of the IEEE Control System Society since January 2008.

Abstract:

Switched systems with positivity constraints arise in various areas. They have been fruitfully employed to model consensus problems, biological systems dynamics and, recently, viral mutation dynamics underdrug treatment.

The theory of "positive switched systems" is rather challenging and offers quite a number of interesting open problems.

In the talk, we will illustrate the main results available as far asstability, stabilizabilty and controllability issues are concerned. Someopen problems will be proposed, and some applications of this generaltheory in the area of biological systems will be illustrated.

Randomization in Systems and Control

Lecture Details

Date:

2010

Location Information:

IEEE 2010 CDC Plenary Lecture

Author:

Marco C. Campi

Author Bio:

Marco Claudio Campi is Professor of Automatic Control at the University of Brescia, Italy.

In 1988, he received the doctor degree in electronic engineering from the Politecnico di Milano, Milano, Italy. From 1988 to 1989, he was a Research Assistant at the Department of Electrical Engineering of the Politecnico di Milano. From 1989 to 1992, he worked as a Researcher at the Centro di Teoria dei Sistemi of the National Research Council (CNR) in Milano and, in 1992, he joined the University of Brescia, Brescia, Italy. He has held visiting and teaching positions at many universities and institutions including the Australian National University, Canberra, Australia; the University of Illinois at Urbana-Champaign, USA; the Centre for Artificial Intelligence and Robotics, Bangalore, India; the University of Melbourne, Australia; the Kyoto University, Japan.

Prof. Campi is an Associate Editor of Systems and Control Letters, and a past Associate Editor of Automatica and the European Journal of Control. From 2002 to 2008, he served as Chair of the Technical Committee IFAC on Stochastic Systems (SS) and he is currently vice-chair for theTechnical Committee IFAC on Modeling, Identification, and Signal Processing (MISP). Moreover, he has been a distinguished lecturer of the Control Systems Society. Marco Campi's doctoral thesis was awarded the "Giorgio Quazza" prize as the best original thesis for year 1988. In 2008, he received the IEEE CSS George S. Axelby outstanding paper award for the article "The Scenario Approach to Robust Control Design", co-authored with G. Calafiore.

The research interests of Marco Campi include: randomized methods, robust convex optimization, system identification, adaptive and data-based control, robust control and estimation, and learning theory.

Abstract:

Designs in systems and control are traditionally carried out through deterministic algorithms consisting of a sequence of steps set by deterministic rules. This approach, however, can be generalized by the introduction of randomization: a randomized algorithm is an algorithm where one or more steps are based on a random rule, that is – among many deterministic rules – one rule is selected according to a random scheme. Randomization has turned out to be a powerful tool for solving a number of problems deemed unsolvable with deterministic methods.

A crucial fact is that randomization permits one to introduce the notion of ``probabilistically successful algorithm''. In many cases, when deterministic successfulness cannot be achieved, probabilistic successfulness offers a valid alternative.

In the talk, the use of randomized algorithms will be discussed in relation to several problems:

robust design (e.g. by means of linear matrix inequalities - LMIs) is computationally difficult. Randomized methods come into play by offering alternative design methodologies;
in estimation and identification, randomization can change our perception of what constitutes an impossible problem, and estimation problems which were deemed impossible can be successfully solved in a probabilistic sense;
trading robustness for performance is a central issue in decision making and new methods for achieving a compromise can be obtained through randomization.

The Role of Theory in Control Practice

Lecture Details

Date:

2010

Location Information:

IEEE 2010 CDC Bode Lecture

Author:

Manfred Morari

Author Bio:

Manfred Morari was appointed head of the Department of Information Technology and Electrical Engineering at ETH Zurich in 2009. He was head of the Automatic Control Laboratory from 1994 to 2008. Before that he was the McCollum-Corcoran Professor of Chemical Engineering and Executive Officer for Control and Dynamical Systems at the California Institute of Technology. He obtained a diploma from ETH Zurich and a Ph.D. from the University of Minnesota, both in chemical engineering. His interests are in hybrid systems and the control of biomedical systems. In recognition of his research contributions he received numerous awards, among them the Donald P. Eckman Award and the John R. Ragazzini Award of the Automatic Control Council, the Allan P. Colburn Award and the Professional Progress Award of the AIChE, the Curtis W. McGraw Research Award of the ASEE, Doctor Honoris Causa from Babes-Bolyai University, Fellow of IEEE, the IEEE Control Systems Field Award, and was elected to the National Academy of Engineering (U.S.). Manfred Morari has held appointments with Exxon and ICI plc and serves on the technical advisory boards of several major corporations.

Abstract:

Twenty years ago I delivered a plenary lecture with the same title at the ACC in Boston. I will go back and reflect on the successes and failures, on what we have learned and which problems remain open. The focus will be on robust and constrained control and the real time implementation of control algorithms. I will comment on the progress we have made on the control of hybrid systems and how our vastly more powerful computational resources have affected the design tools we have at our disposal. Throughout the lecture, industrial examples from the automotive and power electronics domains and the industrial energy sector will illustrate the arguments.

Why Should I Care About Stochastic Hybrid Systems?

Lecture Details

Date:

2010

Location Information:

IEEE 2010 CDC Semi-Plenary Lecture

Author:

Joao Hespanha

Author Bio:

João P. Hespanha received the Licenciatura in electrical and computer engineering from the Instituto Superior Técnico, Lisbon, Portugal in 1991 and the Ph.D. degree in electrical engineering and applied science from Yale University, New Haven, Connecticut in 1998. From 1999 to 2001, he was Assistant Professor at the University of Southern California, Los Angeles. He moved to the University of California, Santa Barbara in 2002, where he currently holds a Professor position with the Department of Electrical and Computer Engineering. Prof. Hespanha is Associate Director for the Center for Control, Dynamical-systems, and Computation (CCDC), Vice-Chair of the Department of Electrical and Computer Engineering, and a member of the Executive Committee for the Institute for Collaborative Biotechnologies (ICB). From 2004—2007 he was an associate editor for the IEEE Transactions on Automatic Control.

His current research interests include hybrid and switched systems; the modeling and control of communication networks; distributed control over communication networks (also known as networked control systems); the use of vision in feedback control; and stochastic modeling in biology.

Dr. Hespanha is the recipient of the Yale University’s Henry Prentiss Becton Graduate Prize for exceptional achievement in research in Engineering and Applied Science, a National Science Foundation CAREER Award, the 2005 best paper award at the 2nd Int. Conf. on Intelligent Sensing and Information Processing, the 2005 Automatica Theory/Methodology best paper prize, the 2006 George S. Axelby Outstanding Paper Award, and the 2009 Ruberti Young Researcher Prize. Dr. Hespanha is a Fellow of the IEEE and an IEEE distinguished lecturer since 2007.

Abstract:

Hybrid systems combine continuous-time dynamics with discrete modes of operation. The states of such system usually have two distinct components: one that evolves continuously, typically according to a differential equation; and another one that only changes through instantaneous jumps.

We present a model for Stochastic Hybrid Systems (SHSs) where transitions between discrete modes are triggered by stochastic events, much like transitions between states of a continuous-time Markov chains. However, in SHSs the rate at which transitions occur depends on both the continuous and the discrete states of the hybrid system. The combination of continuous dynamics, discrete events, and stochasticity results in a modeling framework with tremendous expressive power, making SHSs appropriate to describe the dynamics of a wide variety of systems. This observation has been the driving force behind the several recent research efforts aimed at developing tools to analyze these systems.

In this talk, we use several examples to illustrate the use of SHSs as a versatile modeling tool to describe dynamical systems that arise in distributed control and estimation, networked control systems, molecular biology, and ecology. In parallel, we will also discuss several mathematical tools that can be used to analyze such systems, including the use of the extended generator, Lyapunov-based arguments, infinite-dimensional moment dynamics, and finite-dimensional truncations.

Controller Design: Getting from Theory to Practice

Lecture Details

Date:

1992

Location Information:

31st IEEE Conference on Decision and Control, Tucson, AZ, December 1992

Author:

Brian Anderson

Author Bio:

Brian D.O. Anderson was born in Sydney, Australia, and received hisundergraduate education at the University of Sydney and PhD at Stanford University. Following completion of his education, he workedin industry in Silicon Valley and served as an Assistant Professor inthe Department of Electrical Engineering at Stanford. He wasFoundation Professor of Electrical Engineering at the University ofNewcastle, Australia, from 1967 until 1981 and is now Professor ofSystems Engineering at the Australian National University. Hisinterests are in control and signal processing. He is a Fellow of theRoyal Society, the Australian Academy of Science, the AustralianAcademy of Technological Sciences and Engineering, and the Instituteof Electrical and Electronic Engineers, and an Honorary Fellow of theInstitution of Engineers, Australia. He holds a doctorate (honoriscausa) from the Universite Catholique de Louvain, Belgium. He isserving a term as President of the International Federation ofAutomatic Control from 1990 to 1993.

Abstract:

Modern controller design packages often fall short of offering what is truly practical: low order controllers, discrete-time controllers operating in a sampled-data loop, and finite word length realizations of controllers with the FWL property minimally impacting closed-loop performance. The lecture describes how to achieve these objectives.

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Other:

An article based on Brian Anderson's Bode Lecture was published in IEEE Control Systems Magazine in August 2003 and is available on IEEE Xplore at http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=229554

New Opportunities for Control: Quantum Internal Model Principle and Decoherence Control

Lecture Details

Date:

2009

Location Information:

48th IEEE Conference on Decision and Control, Shanghai, China, Dec. 2009

Author:

Tzyh Jong Tarn

Author Bio:

He is currently a Professor in the Department of Electrical and Systems Engineering and the Director of the Center for Robotics and Automation at Washington University. He also is the director of the Center for Quantum Information Science and Technology at Tsinghua University. An active member of the IEEE Robotics and Automation Society, Dr. Tarn served as the President of the IEEE Robotics and Automation Society, 1992-1993, the Director of the IEEE Division X (Systems and Control), 1995-1996, and a member of the IEEE Board of Directors, 1995-1996. He is the first recipient of the Nakamura Prize at the 10th Anniversary of IROS in Grenoble, France, 1997, the recipient of the prestigious Joseph F. Engelberger Award of the Robotic Industries Association in 1999, the Auto Soft Lifetime Achievement Award in 2000, the Pioneer in Robotics and Automation Award in 2003 from the IEEE Robotics and Automation Society, and the George Saridis Leadership Award from the IEEE Robotics and Automation Society in 2009. He was featured in the Special Report on Engineering of the 1998 Best Graduate School issue of US News and World Report and his recent research accomplishments were reported in the Washington Times, Washington D.C., the Financial Times, London, Le Monde, Paris, and the Chicago Sun-Times. Dr. Tarn is a Fellow of IEEE and an IFAC Fellow.

Abstract:

Decoherence, which is caused due to the interaction of a quantum system with its environment plagues all quantum systems and leads to the loss of quantum properties that are vital for quantum computation and quantum information processing. Superficially, this problem appears to be the disturbance decoupling problem in classical control theory. In this talk first we briefly review recent advances in Quantum Control. Then we propose a novel strategy using techniques from geometric systems theory to completely eliminate decoherence and also provide conditions under which it can be done so. A novel construction employing an auxiliary system, the bait, which is instrumental to decoupling the system from the environment, is presented. This literally corresponds to the Internal Model Principle for Quantum Mechanical Systems which is quite different from the classical theory due to the quantum nature of the system. Almost all the earlier work on decoherence control employ density matrix and stochastic master equations to analyze the problem. Our approach to decoherence control involves the bilinear input affine model of quantum control system which lends itself to various techniques from classical control theory, but with non-trivial modifications to the quantum regime. This approach yields interesting results on open loop decouplability and Decoherence Free Subspaces (DFS). The results are also shown to be superior to the ones obtained via master equations. Finally, a methodology to synthesize feedback parameters itself is given, that technology permitting, could be implemented for practical 2-qubit systems performing decoherence free Quantum Computing. Open problems and future directions in quantum control also will be discussed.

Support Files

PDF Slides:

Tarn.pdf

Towards a System Theoretic Foundation for Control over Networks

Lecture Details

Date:

2009

Location Information:

48th IEEE Conference on Decision and Control, Shanghai, China, Dec. 2009

Author:

P.R. Kumar

Author Bio:

P. R. Kumar obtained his B. Tech. degree in Electrical Engineering (Electronics) from I.I.T. Madras in 1973, and the M.S. and D.Sc. degrees in Systems Science and Mathematics from Washington University, St. Louis, in 1975 and 1977, respectively. From 1977-84 he was a faculty member in the Department of Mathematics at the University of Maryland Baltimore County. Since 1985 he has been at the University of Illinois, Urbana-Champaign, where he is currently Franklin W. Woeltge Professor of Electrical and Computer Engineering, Research Professor in the Coordinated Science Laboratory, Research professor in the Information Trust Institute, and Affiliate Professor of the Department of Computer Science.

He has worked on problems in game theory, adaptive control, stochastic systems, simulated annealing, neural networks, machine learning, queueing networks, manufacturing systems, scheduling, and wafer fabrication plants. His current research interests are in wireless networks, sensor networks, and networked embedded control systems. He has received the Donald P. Eckman Award of the American Automatic Control Council, the IEEE Field Award in Control Systems, and the Fred W. Ellersick Prize of the IEEE Communications Society. He is a Fellow of the IEEE, and member of the US National Academy of Engineering. He was awarded a Doctor of Science, Honoris Causa, by Eidgenossische Technische Hochschule, Zurich in 2008.

Abstract:

We address several issues that are important for developing a comprehensive understanding of the problems of control over networks. Proceeding from bottom to top, we describe theoretical frameworks to study the following issues, and present some answers:
(i) Network information theory:
Are there limits to information transfer over wireless networks? How should nodes in a network cooperate to achieve information transfer?
(ii) In-network information processing:
How should data from distributed sensors be fused over a wireless network? Can one classify functions of sensor data vis-a-vis how difficult they are to compute over a wireless network?
(iii) Real-time scheduling over wireless networks:
How should packets with hard deadlines be scheduled for transmission over unreliable nodes? What QoS guarantees can be provided with respect to latencies and throughputs?
(iv) Clock synchronization over wireless networks:
What are the ultimate limits to synchronization error? How should clocks be synchronized?
(v) System level guarantees in networked control:
How can one provide overall guarantees on of networked control systems that take into account hybrid behavior, real-time interactions, and distributed aspects?
(vi) Abstractions and architecture:
What are appropriate abstractions, and what is an appropriate architecture, to simplify networked control system design and deployment?

Support Files

PDF Slides:

Kumar.pdf

Mean Field Stochastic Control

Lecture Details

Date:

2009

Location Information:

48th IEEE Conference on Decision and Control, Shanghai, China, Dec. 2009

Author:

Peter E. Caines

Author Bio:

Peter Caines received the BA in mathematics from Oxford University in 1967 and the PhD in systems and control theory in 1970 from Imperial College, University of London, where his supervisor was D. Q. Mayne, FRS. After periods as a postdoctoral researcher and faculty member at UMIST, Stanford, UC Berkeley, Toronto and Harvard, he joined McGill University, Montreal, in 1980, where he is James McGill Professor and Macdonald Chair in the Department of Electrical and Computer Engineering.

Peter Caines is a Fellow of the IEEE, SIAM and the Canadian Institute for Advanced Research, and was elected to the Royal Society of Canada in 2003. In 2000 the adaptive control paper he coauthored with G. C. Goodwin and P. J. Ramadge (IEEE Transactions on Automatic Control, 1980) was recognized by the IEEE Control Systems Society as one of the 25 seminal control theory papers of the 20th century. Peter Caines has served as an Associate Editor of the IEEE Transactions on Automatic Control, the IEEE Transactions on Information Thoory and the SIAM Journal on Control and Optimization; during 1992 - 95 he served on the Board of Governors of the IEEE Control Systems Society, was a Member of the Scientific Advisory Board of the Max Plank Society, 2002 - 2007 and the EU HYCON Review Committee, 2005 - 2007. Peter Caines is the author of Linear Stochastic Systems, John Wiley, 1988, and is the co-editor of several volumes of papers on stochastic systems. His research interests include the areas of system identification, adaptive control, logic control and discrete event systems. Recently his activities have focused on hybrid systems theory, and stochastic multi-agent and distributed systems theory, together with their links to physics, economics and biology.

Abstract:

This talk presents the Mean Field (or Nash Certainty Equivalence (NCE)) methodology initiated with Min-Yi Huang and Roland Malhamé for the analysis and control of large population stochastic dynamic systems. Optimal control problems for multi-agent stochastic systems, in particular those with non-classical information patterns and those with intrinsic competitive behavior, are in general intractable. Inspired by mean field approximations in statistical mechanics, we analyse the common situation where the dynamics and rewards of any given agent are influenced by certain averages of the mass multi-agent behavior. The basic result is that classes of such systems possess game theoretic (Nash) equilibria wherein each agent employs feedback control laws depending upon both its local state and the collectively generated mass effect. In the infinite population limit the agents become statistically independent, a phenomenon related to the propagation of chaos in mathematical physics.

Explicit solutions in the linear quadratic Gaussian (LQG) - NCE case generalize classical LQG control to the massive multi-agent situation, while extensions of the Mean Field notion enable one to analyze a range of problems in systems and control. Specifically, generalizations to nonlinear problems may be expressed in terms of controlled McKean-Vlasov Markov processes, while localized (or weighted) mean field extensions, the effect of possible major players and adaptive control generalizations permit applications to microeconomics, biology and communications; furthermore, the standard equations of consensus theory, which are of relevance to flocking behavior in artificial and biological systems, have been shown to be derivable from the basic LQG - NCE equations. In the distinct point process setting, the Mean Field formulation yields call admission control laws which realize competitive equilibria for complex communication networks.

In this talk we shall motivate the Mean Field approach to stochastic control, survey the current results in the area by various research groups and make connections to physics, biology and economics.

This talk presents joint work with Minyi Huang and Roland Malhamé, and Arman Kizilkale, Arthur Lazarte, Zhongjing Ma and Mojtaba Nourian.

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PDF Slides:

Caines.pdf

Poisson Processes and the Design of Finite State Controllers

Lecture Details

Date:

2009

Location Information:

48th IEEE Conference on Decision and Control, Shanghai, China, Dec. 2009

Author:

Roger W. Brockett

Author Bio:

Roger Brockett is the An Wang professor of electrical engineering and Computer Science at Harvard University. He has been exploring questions in engineering and applied sciences since starting graduate school, and has been teaching since his appointment as an Assistant Professor at MIT in 1963. His contributions include early work on frequency domain stability theory (multipliers), circle criterion instability, differential geometric methods in nonlinear control, feedback linearization and stabilization, the computation of Volterra series, a Lie algebra approach to the sufficient statistics problem in nonlinear estimation, work on robot kinematics and dynamics, formal languages for motion control, hybrid systems, flows for computation related to integrable systems, sub-Riemannian geometry, minimum attention control, quantum control, quantized systems and, most recently, optimal control of Markov processes. He is a fellow of the IEEE and of SIAM, has received awards from IEEE (Control Systems Science and Engineering), ASME (Oldenberger), SIAM (Reid Prize), and AACC (Eckman, and Bellman), is a member of the US National Academy of Engineering. He is this year recipient of the IEEE Leon Kirchmayer Award for Graduate Education. He has directed approximately 60 Ph.D. theses and authored about 200 research papers.

Abstract:

It is widely recognized that many of the most important challenges faced by control engineers involve the development of methods to design and analyze systems having components most naturally described by differential equations interacting with components best modeled using sequential logic. This situation can arise both in the development of high volume, cost sensitive, consumer products and in the design and certification of one of a kind, complex and expensive systems. The response of the control community to this challenge includes work on limited communication control, learning control, control languages, and various efforts on hybrid systems. This work has led to important new ideas but progress has been modest and the more interesting results seem to lack the kind of unity that would lead to a broadly inclusive theory. In this talk we describe an approach to problems of this type based on sample path descriptions of finite state Markov processes and suitable adaptations of known results about linear systems. The result is an insightful design technique yielding finite state controllers for systems governed by differential equations. We illustrate with concrete examples.

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PDF Slides:

Brockett.pdf

Pursuit and Cohesion: In Nature and by Design

Lecture Details

Date:

2007

Location Information:

Plenary lecture, IEEE Conference on Decision and Control, New Orleans, U.S.A., December 2007

Author:

P. S. Krishnaprasad

Author Bio:

P. S. Krishnaprasad received the Ph.D. degree from Harvard University in 1977. He taught in the Systems Engineering Department at Case Western Reserve University from 1977 to 1980. Since August 1980, he has been with the University of Maryland, where he now holds the position of Professor of Electrical & Computer Engineering, with a joint appointment at the Institute for Systems Research. He also participates in the Program in Applied Mathematics and Scientific Computation, and the Program in Neuroscience and Cognitive Science. He has held short or long term visiting positions at Erasmus University, the University of Groningen, the University of California Berkeley, Caltech, Centre Interfacultaire Bernoulli in EPFL, Cornell University and Princeton University.

Krishnaprasad's interests lie in the broad areas of geometric control theory, filtering and signal processing theory, robotics, acoustics, and biologically-inspired approaches to control, sensing and computation. He has made contributions to system identification, geometric mechanics, languages for robotics, actuation based on smart materials, and control of collectives. His current work is focused on pursuit and cohesion in nature and in engineered systems.

P. S. Krishnaprasad was elected a Fellow of the IEEE in 1990. He was appointed a 1998-2000 Distinguished Faculty Research Fellow of the University of Maryland. He was the Munich Mathematical Colloquium Lecturer in October 2006.

Abstract:

Pursuit phenomena in nature have a vital role in survival of species. In addition to prey-capture and mating behavior, pursuit phenomena appear to underlie territorial battles in certain species of insects. In this talk we discuss the geometric patterns in certain pursuit and prey capture phenomena in nature, and suggest sensorimotor feedback laws that explain such patterns. Our interest in this problem first arose from the study of a motion camouflage (stealth) hypothesis due to Srinivasan and his collaborators, and an investigation of insect capture behavior in the FM bat Eptesicus fuscus, initiated by Cynthia Moss. Models of interacting particles, developed in collaboration with Eric Justh, prove effective in formulating and deriving biologically plausible steering laws that lead to observed patterns.

The echolocating bat E. fuscus perceives the world around it in the dark, primarily through the information it gathers rapidly and dependably by probing the environment through controlled streams of pulses of frequency modulated ultrasound. The returning echoes from scatterers such as obstacles (cave walls, trees), predators (barn owls) and prey (insects), are captured and transduced into neuronal spike trains by the highly sensitive auditory system of the bat, and processed in the sensorimotor pathways of the brain to steer the bat’s flight in purposeful behavior. In joint work with Kaushik Ghose, Timothy Horiuchi, Eric Justh, Cynthia Moss, and Viswanadha Reddy, we have begun to understand the control systems guiding the flight. The effectiveness of the bat in coping with, attenuation and noise, uncertainty of the environment, and sensorimotor delay, makes it a most interesting model system for engineers concerned with goal-directed and resource-constrained information processing in robotics. The bat’s neural realizations of auditory-motor feedback loops may serve as models for implementations of algorithms in robot designs.

While the primary focus of this talk is on pursuit, the results suggest ways to synthesize interaction laws that yield cohesion in collections of particles, treating pursuit as a building block in mechanisms for flocking in nature and in engineered systems.

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PDF Slides:

Krishnaprasad - PDF.pdf