Group for Research in Decision Analysis

Multi-Modal Control: Complexity, Expressiveness, and Optimal Control

Magnus Egerstedt

In order to manage the growing complexity associated with many modern control applications, a divide-and-conquer approach has emerged as a possible solution. The main idea is to identify a number of control modes, defined with respect to particular tasks, sensory sources, or operating points, and then combine these modes together in a suitable manner.

This talk focuses on two questions in the area multi-modal control, namely 1) Can one obtain suitable continuous control modes from empirical data?, and 2) Given a set of such modes, how should they be stringed together? To answer the first of these questions a trade-off has to be found between the complexity of the control modes, and their expressiveness, while the second question relies on the development of a computational framework for optimal control of hybrid systems. Applications in robotics and biological systems (including groups of ants and schooling fish) will be examined and related to the theoretical developments.

Bio: Magnus B. Egerstedt was born in Stockholm, Sweden, and is an Assistant Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology. He received the M.S. degree in Engineering Physics and the Ph.D. degree in Applied Mathematics from the Royal Institute of Technology, Stockholm, in 1996 and 2000 respectively. He also received a B.A. degree in Philosophy from Stockholm University in 1996. He spent 2000-2001 as a Postdoctoral Fellow at the Division of Engineering and Applied Science at Harvard University and during 1998 he was a Visiting Scholar at the Robotics Laboratory at the University of California, Berkeley. Dr. Egerstedt's research interests include optimal control as well as modeling and analysis of hybrid and discrete event systems, with emphasis on motion planning and control of (teams of) mobile robots. He is a Senior Member of the IEEE, and he received the CAREER award from the National Science Foundation in 2003.