IEEE CSS Distinguished Lectures Hosted by the Chapter in 2008

Funded by the IEEE Control Systems Society (CSS), the CSS Distinguished Lecture Series is primarily set up to help Society chapters provide interesting and informative programs for the membership, but the Series may also be of interest to industry, universities, and other parties. The IEEE Singapore Control Systems Chapter features the following distinguished lectures by Professor Li Qiu of the Hong Kong University of Science and Technology on September 19, 2008, and by Professor Iven Mareels of the University of Melbourne on October 28, 2008:


Speaker of the 2nd IEEE CSS Distinguished Lecture in 2008 (October 28, 2008)


Professor Iven Mareels was born in Aalst Belgium on 11 August 1959. He obtained the (ir) Masters of Electromechanical Engineering from Gent University, Belgium in 1982 and the PhD in Systems Engineering from the Australian National University, Canberra, Australia in 1987. Since 1996, he is a Chair Professor of Electrical and Electronic Engineering in the Department of Electrical and Electronic Engineering, the University of Melbourne. In June 2007, he became Dean of the School of Engineering. His research interests are in adaptive and learning systems, nonlinear control and modeling. At present he has strong research interests in modeling and controlling of large scale systems, both engineered as well as natural systems, with a particular interest in modeling and control of epilepsy. Prof. Iven Mareels has published widely, 3 research monographs, in excess of 100 journal publications and 150 conference publications. He holds 3 international patents. He has supervised to completion 25 PhD students and is currently supervising 9 PhD students.


The Lecture: Control Theory Meets Neuroscience


From its very onset cybernetics was interested in communication and control as it is implemented in either engineered systems or biology. Indeed much of the seminal work of Norbert Wiener, John Von Neumann and Claude Shannon was motivated by the fact that their theories and insight would lead to elucidating how the mind works.


This section reviews some of the basic questions they were interested in, like memory capacity and computational capacity of the brain, and compare some of their predictions with measurements and neuroscience estimates that have been made more recently.


Of particular interest to systems theory people is the fact that in order to elucidate how learning works, especially in motor control, neuroscience researchers are particularly interested in motor control in interactions with the environment that are described as “unstable”, like drilling or writing or standing/walking. Indeed in view of recent advances in our understanding of feedback entropy and invariance entropy, control theory can shed light on the minimum required feedback information rate that is necessary for a stable feedback loop to exist, and may settle the question on whether or not a feedback loop exists. The latter is of great interest, for example the “simple” question, Do humans use feedback during walking?” is still considered an open question.


The notion of feedback entropy is presented, and illustrated for both linear and nonlinear feedback systems. It is applied to an experimental model of gross motor for writing and walking. This leads to a lower bound estimate of the bit rate capacity of the typical neural circuits that support gross motor movement.



Speaker of the 1st IEEE CSS Distinguished Lecture in 2008 (September 19, 2008)


Professor Li Qiu received the B.Eng. degree from Hunan University, Changsha, Hunan, China, in 1981, and the M.A.Sc. and Ph.D. degrees from the University of Toronto, Toronto, Ont., Canada, in 1987 and 1990, respectively, all in electrical engineering. He joined Hong Kong University of Science and Technology, Hong Kong SAR, China, in 1993, where he is now a professor of Electronic and Computer Engineering.


Professor Qiu’s research interests include system, control, information theory, and mathematics for information technology. He served as an associate editor of the IEEE Transactions on Automatic Control and an associate editor of Automatica. He is now a Distinguished Lecturer of IEEE Control Systems Society and the general chair of the 7th Asian Control Conference, which is to be held in Hong Kong in 2009. He is a fellow of IEEE.


The Lecture: Measure of Instability and Multivariable Networked Stabilization with Channel Resource Allocation


In this talk, we will survey the history of an instability measure of an LTI system and its connections with various feedback control problem. Then we will present its connections to networked control problems of multivariable systems. In such problems, communication resource allocation among various signal transmission channels becomes a design issue in addition to the usual controller design.


We will see that some optimal and robust control problems arising in networked control are nontraditional and highly nonconvex but can be nicely and analytically solved, and the solutions are given in terms of the instability measure.


The results to be reported are the recent findings in the joint research with Professor Guoxiang Gu of LSU.


IEEE CSS Distinguished Lectures Hosted by the Chapter in 2007

Speaker of the 2nd IEEE CSS Distinguished Lecture in 2007 (May 17, 2007)

Professor Carlos E. de Souza was born in João Pessoa, Brazil. He received the B.E. degree in Electrical Engineering from the Federal University of Pernambuco, Brazil, in 1976 and the doctoral degree from the University Pierre & Marie Curie, Paris, France, in 1980. From 1980-1984 he was a Lecturer at the Department of Electrical Engineering, Federal University of Uberlândia, Brazil. In 1985 he moved to the Department of Electrical and Computer Engineering, University of Newcastle, Australia, as Lecturer and became Associate Professor in 1997. Since 1998, he is Professor at the Department of Systems and Control, National Laboratory for Scientific Computing (LNCC), Brazil, and is currently Head of Department. During a 1992-1993 sabbatical, he was a visiting professor at the Laboratoire d'Automatique de Grenoble, France. He has also held numerous short-term visiting appointments at universities in several countries, including USA, France, Switzerland, Israel, Australia, and Brazil. He was subject editor for the International Journal of Robust and Nonlinear Control (IJRNC), guest editor for the IJRNC Special Issue on H and Robust Filtering, member of the Editorial Board of the IJRNC and IEE Proceedings Control Theory and Applications, and Chairman of the IFAC Technical Committee on Linear Control Systems (2002-2005). He is a Distinguished Lecturer of the IEEE Control Systems Society and is currently serving as member of the IFAC Council. Prof. de Souza is Fellow of the IEEE and Fellow of the Brazilian Academy of Sciences. His research interests include robust signal estimation, H2 and H filtering, robust control, H2 and H control, Markov jump systems, and time-delay systems. He has published over 200 peer reviewed scientific papers.

The Lecture: Robust State Estimation for Uncertain Dynamical Systems

One of the fundamental problems in control systems is the estimation of the state variables of a dynamic system using available noisy measurements. Estimation methods in the minimum variance sense, i.e. the celebrated Kalman filtering, and in the minimax sense, i.e. H-infinity filtering, have been developed in the past decades. These methods rely on the knowledge of a perfect dynamic model for the signal generating system in order to provide a guaranteed performance. In many cases, however, only an approximate model of the system is available and, in such situations, these methods can fail to provide an acceptable performance. This lecture is concerned with the problem of robust state estimation for dynamic systems subject to parameter uncertainty in the system state-space model. First, the filtering problem and traditional filter designs will be reviewed. Then, design methods of robust filters with an optimized guaranteed performance, in spite of large parameter uncertainty, will be discussed.


Speaker of the 1st IEEE CSS Distinguished Lecture in 2007 (March 13, 2007)


Professor Thomas Parisini was born in Genoa, Italy, in 1963. He received the ``Laurea'' degree (Cum Laude and printing honours) in Electronic Engineering from the University of Genoa in 1988 and the Ph.D. degree in Electronic Engineering and Computer Science in 1993.  From 1988 to 1995, he was with the Dept. of Communications, Computer and Systems Sciences (DIST), University of Genoa. In 1995, he joined the Dept. of Electrical, Electronic and Computer Engineering (DEEI), University of Trieste, as an assistant professor, and in 1998, he joined the Dept. of Electronic and Information Engineering (DEI), Politecnico di Milano, as associate professor. In 2001 he was appointed full professor and Danieli Endowed Chair of Automation Engineering at the Dept. of Electrical, Electronic and Computer Engineering (DEEI), University of Trieste.  Thomas Parisini is the present Chair of the IEEE Control Systems Society Conference Editorial Board. He was the Chair of the Technical Committee on Intelligent Control and an appointed member of the Board of Governors of the IEEE Control Systems Society.  He is a Distinguished Lecturer of the IEEE Control Systems Society.  He is the co-recipient of the 2004 Outstanding Paper Award of the IEEE Trans. on Neural Networks. Thomas Parisini is currently serving as an Associate Editor of Automatica, of the Int. J. of Control, and as Subject Editor of the Int. J. of Robust and Nonlinear Control and served as Associate Editor of the IEEE Trans. on Automatic Control, of the IEEE Trans. on Neural Networks, and as Subject Editor of the Int. J. of Adaptive Control and Signal Processing. He was the Guest Editor of the IEEE Trans. on Neural Networks - Special Issue on Adaptive Learning Systems in Communication Networks and he is currently Guest Editor of the IEEE Trans. on Neural Networks - Special Issue on Neural Networks for Feedback Control.


He was involved in the organization and in the technical program committees of several IEEE CSS sponsored conferences including the IEEE Conf. on Decision and Control and the IEEE Conf. on Control Applications. In particular, he was Vice-Program Chair of the 2003 IEEE Conf. on Decision and Control, 2003, the Program Chair of the IEEE Int. Symp. on Intelligent Control, held in Mexico City, 2001 and the Program Chair of the Joint IEEE Int. Symp. on Intelligent Control and Mediterrean Control Conference held in Limassol, Cyprus, June 2005. He was recently appointed as Program Chair of the 47th IEEE Conf. on Decision and Control to be held in Cancun, MX, in 2008. His research interests include neural-network approximations for optimal control and filtering problems, fault diagnosis for nonlinear systems, hybrid control systems and control of distributed systems. From an application point of view, he is involved as Project Leader in several projects funded by the European Union, by the Italian Ministry for Research and by some major process control companies (ABB, Danieli, Duferco, Galileo Avionics among others).

The Lecture: Fault Diagnosis of Nonlinear Uncertain Systems: an Adaptive Learning Approach

The objective of this lecture is give a tutorial and detailed overview of a recent approach to the solution of the challenging problem of fault detection and isolation and of a class of nonlinear uncertain systems. This methodology is based on the design of a monitoring module that provides the information about the detection of a fault and the information about the specific fault that occurred in a class of a priori-specified fault structures. This module is made of a bank of nonlinear adaptive estimators. One of the nonlinear adaptive estimators is the fault detection and approximation estimator (FDAE) used for detecting and approximating faults. An on--line approximation model, typically based on neural approximators, is used in the FDAE. The remaining ones are fault isolation estimators (FIEs) used only after a fault is detected for isolation purposes. Each FIE corresponds to a particular type of fault in pre-secified class.  Under normal operating conditions (without faults), the FDAE is the only one monitoring the system. Once a fault is detected, then the bank of FIEs is activated and the FDAE goes into the mode of approximating the fault function. The case that none of the isolation estimators matches the occurred fault (to some reasonable degree) corresponds to the occurrence of a new and unknown type of fault, and the approximated fault model can then be used to update the fault class and also the bank of isolation estimators. The fault model generated either by the isolation estimators (in the case of a match) or the detection/approximation estimator is used for fault diagnosis and provides the information to be used by the controller module for fault accommodation. In the lecture, a complete analysis of the above scheme will be carried out in a tutorial but rigorous way and some simulation examples will be also reported, showing the practical aspects involved in the use of this recent approach to control of nonlinear uncertain systems in presence of the possible occurrence of faults and malfunctions.


IEEE CSS Distinguished Lectures Hosted by the Chapter in 2005

Speaker of the 2nd IEEE CSS Distinguished Lecture in 2005 (October 14, 2005)

Professor Jie Huang obtained his Ph.D. degree at Johns Hopkins University in 1990. He was a postdoctoral fellow at Johns Hopkins University from September 1990 to July 1991. From August 1991 to August 1995, he worked in industry in USA. He joined the Chinese University of Hong Kong in 1995 as an Associate Professor in Department of Automation and Computer-Aided Engineering, and is now a professor. His research interests include nonlinear control theory and applications, industrial control and automation, neural networks, computer-aided control system design, and guidance and control of flight vehicles. He is the author of the book, Nonlinear Output Regulation: Theory and Applications, SIAM, 2004.

Dr. Huang is an Editor at Large of Communications in Information and Systems, a Subject Editor of International Journal of Robust and Nonlinear Control, and Associate Editor of Journal of Control Theory and Applications. He was Associate Editor of Asian Journal of Control from 1999-2002, and Associate Editor of IEEE Transactions on Automatic Control from 2002 to 2004. He has been the Guest Editor for International Journal of Robust and Nonlinear Control, Asian Journal of Control, and IEEE Transactions on Neural Networks.

Dr. Huang is a Fellow of IEEE, and an IEEE Control System Society Distinguished Lecturer.

The Lecture: Nonlinear Output Regulation: Theory and Applications

Venue: Department of Electrical & Computer Engineering, National University of Singapore
October 14, 2005

Output regulation problem, or alternatively, servomechanism problem, aims to achieve, in addition to closed-loop stability, asymptotic tracking and disturbance rejection in an uncertain system. Thus it poses a more challenging problem than stabilization. Output regulation problem is a general mathematical formulation of many control problems encountered in our daily life including the landing and taking-off of aircraft, orbiting of satellites, speed regulation of motors and so forth. This talk will focus on the robust output regulation problem for nonlinear systems, which has been one of the central problems in control theory since the 1990s. The talk will start from an introduction to the problem of output regulation, and then proceed to highlight the establishment of a general framework that can cast the robust output regulation problem for a given plant into the robust stabilization problem of an augmented plant, thus setting the stage for systematically tackling output regulation with various stability requirements. The theoretical results will be illustrated with applications to some benchmark nonlinear systems. The talk will be closed with some remarks on open issues.

Speaker of the 1st IEEE CSS Distinguished Lecture in 2005 (June 13 & 14, 2005)

Professor Iven Mareels was born in Aalst Belgium 11 August 1959. He obtained the Bachelor of Electromechanical Engineering from Gent University, Belgium in 1982 and the PhD in Systems Engineering from the Australian National University, Canberra, Australia in 1987. He is presently Professor at the Department of Electrical and Electronic Engineering, the University of Melbourne, where he holds the Chair of Electrical and Electronic Engineering, a position he took up in 1996. Previously he was a Reader at the Australian National University (1990-1996), a lecturer at the University of Newcastle (1988-1990) and the University of Gent, Belgium (1986-1988) .

He is Fellow of the Academy of Technological Sciences and Engineering, Australia, a Fellow of the Institute of Electrical and Electronics Engineers (USA), a member of the Society for Industrial and Applied Mathematics, a Fellow of the Institute of Engineers Australia, Vice-Chair of the Asian Control Professors Association. He is a member of the Board of Governors of the Control Systems Society IEEE. He is vice-chair and chair-elect of the National Committee for Automation, Control and Instrumentation, Australia. He is a director of the Bionic Ear Institute, Melbourne, Australia. He is registered with the Institute of Engineers Australia as a professional engineer. He has extensive experience in consulting for both industry and government.

Since 1996, he is a co-editor in chief, together with Prof. A. Antoulas for the international Journal Systems & Control Letters.

Iven Mareels received the Vice-Chancellor's Award for Excellence in Teaching in 1994 from the Australian National University.

Lecture 1: Information Theory and Feedback

In this lecture, we address the question of stabilising an open loop unstable system through a data rate limited communication channel. It turns out that there is a fundamental limit, even when the communication channel is error free. There is a minimum data rate required to be able to stabilize an unstable system, and above which a stabilising feedback law can be constructed.

We review the ideas first in the context of linear systems subject to additive white Gaussian noise in the state transition map and the observation map, where the required data rate is expressed in terms of the unstable eigenvalues of the state transition map. Subsequently we pose the same question in a nonlinear setting. To this end we first define the concept of topological feedback entropy, which is an open-loop system property. It turns out that the minimum required data rate for stabilization is precisely the plant's topological feedback entropy.

Finally we address the issue in the context of a network of dynamically interconnected systems. To illustrate the power of the result we compute the minimum data rate required in the human nervous system to complete the task of walking.

This lecture reports on joint work with Dr. Girish Nair, Prof. R. Evans and Prof. W. Moran.

Lecture 2: Signal Processing and Control Theory in Support of Brain Research

The human brain is the subject to immense research effort around the world. From a medical perspective, the need is pressing, as witnessed by the following widely reported figures (2000 world health data):

  • 75% of the community is affected by brain disorders for some time in their lifetime.
  • 40% of all hospitalisation are linked to brain disorders/diseases.

This lecture provides an overview of the interdisciplinary research the presenter has engaged in over the last two years. It summarises his interaction with researchers from neuro science, psychiatry and physiology and will hopefully encourage others with a background in signal processing and control theory to contribute to the global brain research efforts. The talk focuses on the research questions.

New and old measurement techniques are promising a wealth of new data requiring new and automated techniques for interpretation, and computer assisted diagnostics. We consider two general measurement techniques: magnetic resonance imaging and eeg and review a number of research questions that are in particular enabled by control and/or system theoretic ideas:

  • Construction of a brain function atlas: the parcellation of the brain according to texture is a classic way of building a brain, in particular a brain cortex, atlas. Traditionally this has been completed using dead brain tissue and histology. We examine the potential of using MRI to construct in vivo an individual's brain/cortex function atlas. The algorithms we propose are based on Bayesian estimation techniques and exploit the use of physiology based prior information in order to achieve classification outcomes. New MRI protocols that exploit control theoretic ideas (we view the image measurement process as an optimal control problem, where the trade-off is image quality against time to capture the relevant image area) are also considered.
  • Motor learning and functional MRI: there are a number of simple learning mechanisms proposed in the context of learning of motor functions. Haptic interfaces provide one way of analysing these and testing learning hypotheses. The learning hypotheses proposed in the literature are closely related with classical adaptive control ideas. From the latter literature we can borrow a number of results to underscore the behaviour of such learning rules in the presence of unmodelled uncertainty (which is unavoidably present in learning) and instability phenomena (which may be imposed through the haptic interface). This study may be complemented with recent advances in functional MRI that reveal how the brain organises motor information.
  • Predicting the onset of epilepsy: recent results in the theory of feedback establish minimum data information rates required to execute simple feedback loops that are essential to achieve stable behaviour, independent of the way this feedback is implemented. These results establish lower bounds on the data rate in the nervous system when we execute "open loop unstable" tasks (such as walking, and many other interactions with the environment). Using eeg data and dynamical systems theory we can observe that these minimal data rates cannot be supported by a brain during an epileptic seizure. This leads to an information theoretic interpretation as to the nature of an epileptic seizure.