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Networks and Control: A Small-Gain Theory

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报告人: 姜钟平 教授(纽约大学)

讲座日期:2019-06-26

讲座时间:10:10

报告地点:长安校区 数学与信息科学学院学术交流厅

主办单位:数学与信息科学学院

讲座人简介:

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 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.

讲座简介:

Networks and control are ubiquitous in many branches of engineering and science. The first part of the talk aims to answer the first fundamental question: When is a dynamical network stable? I will present some of our recent results centered around the network/nonlinear small-gain theorems. Then, in the second part of the talk, I will attempt to address the second fundamental question: When can an (unstable) dynamical network be made stable by means of feedback control? As a practical application, I show how the proposed small-gain methodology can be applied to solve the challenging problem of event-triggered control of nonlinear systems, that arises from cyber-physical systems like smart grid and intelligent transportation systems.

 

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