Stan Żak's Bio
Stanisław H. Żak
received his high school diploma from III
L.O. in Białystok in Poland. He received his
Ph.D. degree from Warsaw University of
Technology (Politechnika Warszawska)
in 1977. He was an Assistant Professor in the Institute of Control and
Industrial Electronics (ISEP), Warsaw University of Technology, from 1977
until 1980. From 1980 until 1983, he was a Visiting Assistant Professor in the Department of Electrical Engineering, University
of Minnesota in Minneapolis.
In 1983 he joined the School of Electrical
and Computer Engineering at Purdue University in West Lafayette, IN.
Stan has worked in various areas
of control, optimization, fuzzy control, and neural networks. He is a co-author
with Professor
T. Kaczorek, his Ph.D. advisor, and K. M. Przyłuski of Selected Methods of Analysis of
Linear Dynamical Systems published in 1984 by the Polish Scientific
Publishers (PWN) in Warsaw,
Poland. He is a
co-author with E. K. P. Chong
of An
Introduction to Optimization Methods whose third edition was published in
2008 by Wiley-Interscience. He is an author of Systems
and Control published in 2003 by Oxford University Press. He also has been
a contributor to the Comprehensive
Dictionary of Electrical Engineering published by the CRC Press (now in the
Taylor & Francis Group) whose second edition appeared in 2005. Stan is a
past Associate Editor of Dynamics and Control and the IEEE
Transactions on Neural Networks. He was on the Editorial Board of COMPUTING.
Research interests of Stan have
fallen into three different fields: an algebraic approach to the analysis and
synthesis of linear systems, the control and state estimation of nonlinear
systems, and neural networks and fuzzy systems.
At the beginning of his research
activity, he was using abstract algebra and algebraic geometry to study
structural properties of linear dynamical systems with delays. Together with
Professor Olbrot of Warsaw
University of Technology, he obtained a number of tests for controllability and
observability of time delay systems. Later, with
Professor E. B. Lee at the University
of Minnesota, he used the
above results to generate control algorithms for a class of systems with
delays. Next, with Professors Lee and Lu
at the University
of Minnesota, he
characterized structural invariants of a general class of linear dynamical
systems whose coefficients are from a ring rather than from a field.
In the second stage of his
research activity, Stan was involved with constructing controllers and state
estimators for nonlinear and uncertain dynamical systems. With his Ph.D.
student, B. L.
Walcott, Stan developed a new class of estimators for nonlinear/uncertain
systems. At the same time, he co-authored a tutorial on variable structure
sliding mode control, which is often cited in the circle of researchers in this
area. In this tutorial, among other topics, connections between the theory of
deterministic control of uncertain systems and sliding mode control are
investigated. Later, along with Professor
S. Hui of San Diego State
University, he proposed
bounded robust state and output feedback stabilization controllers for
uncertain dynamical systems.
Currently, Stan has been studying
neural networks and fuzzy systems. In particular, with his collaborators, he
contributed to the understanding of the dynamical behavior of the
Brain-State-in-a-Box (BSB) neural network model. This neural network has been
used to model the effects and mechanisms seen in psychology and the cognitive
sciences. Stan and his co-workers used the BSB neural network to synthesize
novel types of associative memories.
More recently, he became
interested in the applications of neural networks for solving optimization
problems. Together with his Ph.D. student W. E. Lillo and Professor Hui, he developed a neural
network for solving minimum norm problems using a penalty function approach.
Next, with his Ph.D. student M. P. Glazos
and Professor Hui, he used the sliding mode approach
to analyze a class of dynamical systems, called artificial neural networks, that solve convex optimization problems. Then,
collaborating with Professor
Chong and Professor Hui, Stan used the sliding
mode approach to construct and analyze neural networks for solving linear
programming problems. The proposed neural networks solve programming problems
in finite time. Working with his Ph.D. student, H. Y. Chan, he developed a neural
network for solving systems of linear matrix inequalities.
Another current interest of Stan
is fuzzy logic control. With Professor Marcelo C. M. Teixeira of the Universidade Estadual Paulista in Ilha Solteira, Brazil, Stan proposed a novel method
for constructing fuzzy models of dynamical systems and their stabilization.
With former Ph.D. student Tony Will, who is now Senior Project Engineer with
General Motors, Stan has been involved in developing intelligent controllers
for vehicle steering and braking maneuvers. With his Ph.D. student H. Y. Chan,
Stan investigated computational potential of chaotic neural networks.