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Kumpati S. Narendra
Harold W. Cheel Professor of Electrical Engineering
- Phone
- (203) 432-9909
- Fax
- (203) 432-7481
- Mailing Address
- P.O. Box 208267
New Haven, CT 06520
- Office Address
- 10 Hillhouse Avenue
Dunham Laboratory 512
New Haven, CT 06511
Ph.D., Harvard University
RESPONSIBILITIES
Director, Center for Systems Science INTERESTS
Nonlinearities, uncertainty, complexity, and time variations are playing an increasing role in a wide spectrum of problem in new technologies such as robotics, manufacturing, space technology, and medical instrumentation, as well as in older technologies such as process control and aircraft control. New methodologies are needed to cope with them effectively.
It is well known that stability, speed, accuracy, and robustness are the four desirable characteristics in any efficient control system. His research focused on stability theory in the 1960s and on adaptive control and stochastic learning automata in the 1970s and 1980s. In 1988. he became interested in artificial neural networks, and since that time have been investigating how such networks can be used to identify and control nonlinear dynamical systems with uncertainty.
While adaptive systems with neural networks-based controllers can cope with complexity, nonlinearities, and uncertainty, they cannot be directly used in time-varying environments. In the 1990s, to deal with such situations, he initiated research on adaptive control using multiple models. Ten years since its conception, the field has evolved into three distinct but related areas:
i) stability and instability in switching systems,
ii) adaptation in rapidly time-varying environments, and
iii) modeling and control of decentralized autonomous agents.
It is well known that stability, speed, accuracy, and robustness are the four desirable characteristics in any efficient control system. His research focused on stability theory in the 1960s and on adaptive control and stochastic learning automata in the 1970s and 1980s. In 1988. he became interested in artificial neural networks, and since that time have been investigating how such networks can be used to identify and control nonlinear dynamical systems with uncertainty.
While adaptive systems with neural networks-based controllers can cope with complexity, nonlinearities, and uncertainty, they cannot be directly used in time-varying environments. In the 1990s, to deal with such situations, he initiated research on adaptive control using multiple models. Ten years since its conception, the field has evolved into three distinct but related areas:
i) stability and instability in switching systems,
ii) adaptation in rapidly time-varying environments, and
iii) modeling and control of decentralized autonomous agents.
Work is currently in progress in my group in all the above three areas.
AWARDS & HONORS
- Richard E. Bellman Control Heritage Award "for pioneering contributions to stability theory, adaptive and learning systems theory, and for inspiring leadership as mentor, advisor, and teacher over a period spanning four decades." (2003)
REPRESENTATIVE PUBLICATIONS
- Identification and control of a nonlinear discrete-time system based on its linearization: a unified framework, Lingji Chen , Kumpati S Narendra, 2004, IEEE Trans. on Neural Networks, 15(3), 663-673.
- On common quadratic Lyapunov functions for pairs of stable LTI systems whose system matrices are in companion form , R.N. Shorten , Kumpati S Narendra, 2003, IEEE Transactions on Automatic Control, 48(4), 618-621.
- Exact output tracking in decentralized adaptive control systems , Kumpati S Narendra, N.O. Oleng, 2002, IEEE Transactions on Automatic Control, 47(2), 390-395.
- Stochastic adaptive control using multiple models for improved performance in the presence of random disturbances , Kumpati S Narendra, O.A. Driollet, 2001, International Journal of Adaptive Control and Signal Processing, 15(3), 297-317.
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