**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

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

Professor Iven Mareels was born in

**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

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

**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, *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 *H*_{2} and *H*_{∞} filtering, robust control, *H*_{2} 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

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

**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 *Nonlinear Output Regulation: Theory
and Applications*,

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,

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

He is Fellow of the

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

**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.