Learning-Based Control for Uncertain Complex Systems

Center for Urban Science + Progress at NYU Tandon

Can we demonstrate RADP as a new framework for data-driven adaptive and optimal nonlinear control design using autonomous vehicles?

Seminar Room 1325, 12th Floor

NYU CUSP is pleased to host our annual Research Seminar Series, featuring leading voices in the growing field of urban informatics. The seminars will examine real-world challenges facing cities and urban environments around the world, with topics ranging from citizen and social sciences to smart infrastructure.Learning-Based Control for Uncertain Complex SystemsModel-based control has witnessed tremendous progress over the last 100 years. In the era of artificial intelligence and autonomous systems, traditional model-based control-theoretical methods are insufficient to addressing emerging control applications tied to networks of super autonomous systems involving V2X communications and operating in complex dynamically changing environments. Learning-based control is a new topic aimed at developing computationally simple, analytically tractable (reinforcement) learning algorithms. These algorithms yield direct adaptive optimal controllers from data collected online in real time. In this talk, Zhong-Ping Jiang will first review recent developments in adaptive dynamic programming (ADP) for adaptive optimal control of unknown dynamical systems. Then, he will present robust adaptive dynamic programming (RADP) for continuous-time linear and nonlinear systems with dynamic uncertainties. The effectiveness of RADP as a new framework for data-driven adaptive and optimal nonlinear control design is demonstrated via its applications to autonomous vehicles and biological motor control.About the Speaker 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 Ecole des Mines de Paris, France, in 1993. Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His research is in the general fields of dynamical networks and nonlinear control (both model-based and learning-based), with applications to information, mechanical and biological systems.Prof. Jiang is a recipient of the prestigious Queen Elizabeth II Fellowship Award from the Australian Research Council, CAREER Award from the U.S. National Science Foundation, JSPS Invitation Fellowship from the Japan Society for the Promotion of Science, Distinguished Overseas Chinese Scholar Award from the NSF of China, and several best paper awards. He has served as Deputy Editor-in-Chief, Senior Editor and Associate Editor for numerous journals. Prof. Jiang is a Fellow of the IEEE, IFAC, CAA and AAIA, a foreign member of the Academia Europaea (Academy of Europe), and is among the Clarivate Analytics Highly Cited Researchers. In 2022, he received the Excellence in Research Award from the NYU Tandon School of Engineering.**N﻿YU CUSP Research Seminars are only open to faculty, students, and staff at NYU – they are not open to the public at this time.**

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Learning-Based Control for Uncertain Complex Systems

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Learning-Based Control for Uncertain Complex Systems

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