Learning-based Control: A Tool for Autonomous Driving
Professor Zhong-Ping Jiang presents recent developments in learning-based control for completely unknown dynamical systems, and how to learn adaptive optimal controllers from limited data. The progress on the topic benefits tremendously from the entanglement of reinforcement learning and model-based control theory. Learning-based design schemes aim to overcome the well-known “curse of dimensionality” and the “curse of modeling” associated with Bellman’s Dynamic Programming. In addition, thanks to the systematic use of tools from systems and control theory, stability and robustness guarantees are shown for learning-based optimal controllers. The effectiveness of the proposed learning-based control methodology is demonstrated via its application to connected and autonomous vehicles.
Zhong-Ping Jiang received an M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and a Ph.D. in automatic control and mathematics from the Ecole des Mines de Paris (now, called ParisTech-Mines), France, in 1993, under the direction of Prof. Laurent Praly.
Currently, he 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, robust adaptive dynamic programming, reinforcement learning and their applications to information, mechanical and biological systems. In these fields, he has written six books and is author/co-author of over 500 peer-reviewed journal and conference papers.
Dr. Jiang has served as Deputy Editor-in-Chief, Senior Editor and Associate Editor for numerous journals. Prof. Jiang is a Fellow of the IEEE, a Fellow of the IFAC, a Fellow of the CAA and is among the Clarivate Analytics Highly Cited Researchers.
