Bode Lecture, Plenaries, Semi-Plenaries

Bode lecture: The role of theory in control practice

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.

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.

Plenary 1: Tall Transfer Functions, Singular Spectra and Econometric Modelling

Central banks and funds investment managers work with mathematical models. In recent years, a new class of model has come into prominence—generalized dynamic factor models. These are characterized by having a modest number of inputs, corresponding to key economic variables and industry-sector-wide variables for central banks and funds managers respectively, and a large number of outputs, economic time series data or individual stock price movements for example. It is common to postulate that the input variables are linked to the output variables by a finite-dimensional linear time-invariant discrete-time dynamic model, the outputs of which are corrupted by noise to yield the measured data. The key problems faced by central banks or funds managers are model fitting given the output data (but not the input data), and then using the model for prediction purposes.

These are essentially tasks usually considered by those practicing identification and time series modelling. Nevertheless there is considerable underlying linear system theory. This flows from the fact that the underlying transfer function matrix is tall. This presentation will describe a number of consequences of this seemingly trivial fact, and then go on to indicate how to cope with time series with different periodicities, e.g. monthly and quarterly, where multirate signal processing and control concepts are of relevance.

Brian Anderson is Distinguished Professor at the Australian National University and Distinguished Researcher in National ICT Australia. His interests are in currently in formation control, sensor networks and econometric modelling. His past contributions have been in other areas of control as well as circuit theory, signal processing and telecommunications.

He was born in Sydney, Australia, and received his undergraduate education at the University of Sydney, with majors in pure mathematics and electrical engineering. He subsequently obtained a PhD degree in electrical engineering from Stanford University. Following completion of his education, he worked in industry in Silicon Valley and served as a faculty member in the Department of Electrical Engineering at Stanford. He was Professor of Electrical Engineering at the University of Newcastle, Australia from 1967 until 1981.

He is a Fellow of the IEEE, IFAC, Royal Society London, Australian Academy of Science, Australian Academy of Technological Sciences and Engineering, Honorary Fellow of the Institution of Engineers, Australia, and Foreign Associate of the US National Academy of Engineering. He holds doctorates (honoris causa) from the Université Catholique de Louvain, Belgium, Swiss Federal Institute of Technology, Zürich, and the universities of Sydney, Melbourne, New South Wales and Newcastle in Australia. He served as President of the International Federation of Automatic Control from 1990 to 1993 and as President of the Australian Academy of Science between 1998 and 2002. His awards include the IEEE Control Systems Award of 1997, the 2001 IEEE James H Mulligan, Jr Education Medal, and the Guillemin-Cauer Award, IEEE Circuits and Systems Society in 1992 and 2001, the Bode Prize of the IEEE Control System Society in 1992, the Senior Prize of the IEEE Transactions on Acoustics, Speech and Signal Processing in 1986, and other best paper awards.

Plenary 2: Randomization in systems and control: a change of perspective

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.

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.

Semi-plenary 1: Why Should I Care About Stochastic Hybrid Systems?

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.

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.

Semi-plenary 2: Switched systems with positivity constraints: theory, applications and open problems.

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 under drug 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 as stability, stabilizabilty and controllability issues are concerned. Some open problems will be proposed, and some applications of this general theory in the area of biological systems will be illustrated.

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.