报告题目：Learning-Based Control Methods for Resilient Autonomy

报告人： Zhong-Ping Jiang, New York University

报告时间：2026年6月7日15:00

报告地点：仙林校区教四516会议室

主办单位：碳中和先进技术研究院、麻豆av

报告人简介：

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 ParisTech-Mines, France, in 1993.

Currently, he is an Institute Professor in the Department 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, transportation and biological systems.

Prof. Jiang currently serves the IEEE Intelligent Transportation Systems Society’s Board of Governors and leads the Distinguished Lecturer program. He has served as Deputy Editor-in-Chief, Senior Editor and Associate Editor for numerous journals. He is among the Clarivate Analytics Highly Cited Researchers and Stanford’s Top 2% Most Highly Cited Scientists. In 2022, he received the Excellence in Research Award from the NYU Tandon School of Engineering. Prof. Jiang is a foreign member of the Academia Europaea (Academy of Europe) and an ordinary member of the European Academy of Sciences and Arts, and also is a Fellow of the IEEE, IFAC, CAA, AAIA and AAAS.

报告摘要：

Uncertainty, limited modeling knowledge, and adversarial disruptions are unavoidable in real-world engineering applications. This is especially true in emerging cyber-physical transportation systems, where control algorithms must make fast decisions using data while interacting with human drivers, road geometry, communication networks, and software-enabled infrastructure. In this talk, we will present learning-based control methods that move from nominal autonomy to resilient autonomy, with an emphasis on data-driven optimal control, output regulation, and cyber-resilience for safety-critical transportation systems.