Sanjay Lall

Sanjay Lall Headshot Photo
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Sanjay Lall is Professor of Electrical Engineering in the Information Systems Laboratory and Professor of Aeronautics and Astronautics at Stanford University. He received a B.A. degree in Mathematics with first-class honors in 1990 and a Ph.D. degree in Engineering in 1995, both from the University of Cambridge, England. His research group focuses on the development of advanced engineering methodologies for the design of control, optimization and signal processing algorithms which occur in a wide variety of electrical, mechanical and aerospace systems. Before joining Stanford he was a Research Fellow at the California Institute of Technology in the Department of Control and Dynamical Systems, and prior to that he was a NATO Research Fellow at Massachusetts Institute of Technology, in the Laboratory for Information and Decision Systems. He was also a visiting scholar at Lund Institute of Technology in the Department of Automatic Control. He has significant industrial experience applying advanced algorithms to problems including satellite systems at Lockheed Martin, advanced audio systems at Sennheiser, Formula 1 racing, and integrated circuit diagnostic systems, in addition to several startup companies. Professor Lall has served as Associate Editor for the journal Automatica, on the steering and program committees of several international conferences, and as a reviewer for the National Science Foundation, DARPA, and the Air Force Office of Scientific Research. He is the author of over 130 peer-refereed publications.

Stanford University


Paolo Alto, California
United States

Distinguished Lecture Program

Talk Title: Computation of decentralized control systems

In this talk we discuss the problem of constructing decentralized control systems, which is an outstanding problem in control theory. For centralized control systems, there are many effective algorithms for computing controllers, and this is possible for a wide class of systems including deterministic models such as linear dynamical systems and stochastic models such Markov decision processes.

For decentralized control the situation is very different. For many problems where the centralized counterpart is simple, such as control to minimize the mean square error, there are no known computationally tractable algorithms for the decentralized case.

We present an overview of what is known, along with our recent results, in which we show that for certain restricted classes of  problems efficient algorithms for finding optimal controller do exist, and for a more general class of systems one may compute approximately optimal controllers efficiently.