Speaker
Zhong-Ping Jiang (Professor, Electrical and Computer Engineering, Tandon School of Engineering, New York University)
Time & Location
Jul 19 (Fri) 15:00 ~ 16:15 / Building 133 Room 204
Abstract
In this talk, we present a learning-based approach to data-driven optimal control design for completely unknown linear and nonlinear continuous-time systems. Techniques from reinforcement learning, dynamic programming and modern nonlinear control are used to obtain a new class of learning-based adaptive optimal controllers. Then, we show how this data-driven non-model-based control theory can be applied to solve the adaptive optimal control problem for connected autonomous and human-operated vehicles. For simplicity, we consider the scenarios where n human-driven vehicles only transmit motional data and an autonomous vehicle in the tail receives the broadcasted data from preceding vehicles by wireless vehicle-to-vehicle (V2V) communication devices. Considering the cases of range-limited V2V communication and input saturation, several optimal control problems are formulated to minimize the errors of distance and velocity and to optimize the fuel usage. By employing adaptive dynamic programming (ADP) technique, optimal controllers are obtained without relying on the knowledge of system dynamics. Extensions to global nonlinear/adaptive optimal control and adaptive optimal tracking with disturbance rejection are studied. The effectiveness of the proposed approaches is demonstrated via online learning control of connected vehicles in the Paramics’ traffic micro-simulation.
Biography
Zhong-Ping JIANG received the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from the ParisTech-Mines, France, in 1993, under the direction of Prof. Laurent Praly. Dr. Jiang currently is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His main research interests include stability theory, robust/adaptive/distributed nonlinear control, adaptive dynamic programming and their applications to information, mechanical and biological systems. He is coauthor of four books Stability and Stabilization of Nonlinear Systems (with Dr. I. Karafyllis, Springer, 2011), Nonlinear Control of Dynamic Networks (with Drs. T. Liu and D.J. Hill, Taylor & Francis, 2014), Robust Adaptive Dynamic Programming (with Y. Jiang, Wiley-IEEE Press, 2017) and Nonlinear Control Under Information Constraints (with T. Liu, Science Press, 2018). Prof. Jiang is a Deputy co-Editor-in-Chief of the Journal of Control and Decision and of the IEEE/CAA Journal of Automatica Sinica, a Senior Editor of the IEEE Control Systems Letters and the Systems and Control Letters, and has served as an Associate Editor for several journals. Prof. Jiang is an IEEE Fellow, an IFAC Fellow and is a Clarivate Analytics Highly Cited Researcher.
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