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.