The Nonlinear Small-Gain Theory for Networks and Control
#Nonlinear
#systems
#Small-gain
#theory
#Event-triggered
#control
#Feedback
#optimization
#Learning-based
The world is nonlinear and linked. Small-gain theory is one of the most important tools to tackle fundamentally challenging control problems for interconnected linear and nonlinear systems. In this talk, I will first review early developments in nonlinear small-gain theorems and associated nonlinear control design and show how it served as a basic tool to unify numerous results in constructive nonlinear control. Then, I will present recent developments in network/cyclic small-gain theorems for complex large-scale nonlinear systems, with a special focus on event-triggered control and feedback optimization. Finally, I will discuss briefly how machine learning techniques can be invoked to relax the conservativeness of small-gain designs, that falls into the emerging area of learning-based control, a new direction in control theory.
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- 172 St. George St.,
- Toronto, Ontario
- Canada M5R 0A3
- Building: SF B560
- Room Number: SF B560
- Starts 28 October 2025 04:00 AM UTC
- Ends 07 November 2025 05:00 AM UTC
- No Admission Charge
Speakers
Zhong-Ping Jiang of New York University
Topic:
The Nonlinear Small-Gain Theory for Networks and Control
The world is nonlinear and linked. Small-gain theory is one of the most important tools to tackle fundamentally challenging control problems for interconnected linear and nonlinear systems. In this talk, I will first review early developments in nonlinear small-gain theorems and associated nonlinear control design and show how it served as a basic tool to unify numerous results in constructive nonlinear control. Then, I will present recent developments in network/cyclic small-gain theorems for complex large-scale nonlinear systems, with a special focus on event-triggered control and feedback optimization. Finally, I will discuss briefly how machine learning techniques can be invoked to relax the conservativeness of small-gain designs, that falls into the emerging area of learning-based control, a new direction in control theory.
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 ParisTech-Mines, France, in 1993, under the direction of Prof. Laurent Praly.
Currently, he is an Institute Professor in the Department of Electrical and Computer Engineering and an affiliate professor in the Department of Civil and Urban 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 the author/co-author of about 600 peer-reviewed journal and conference papers.
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, and 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.