Distinguished Lecture 5 Iterative Learning Control and Its Application to Multi-Agent Networks
Date/Time Tuesday, May 31, 2016 16:20-17:00
Venue International Leture Hall of the 2nd floor
Presenter Dr. Deyuan Meng, Beihang University, China

Abstract

In this presentation, we discuss the problem of combined studies on iterative learning control and distributed control of multi-agent networks. We first give brief reviews of iterative learning control and multi-agent network control, including backgrounds and basic frameworks of them. Then we discuss how to apply iterative learning control to refine high-precision consensus tracking performances of multiple agents in a networked environment. A distributed iterative learning control algorithm is considered by benefiting from the nearest neighbor-interaction rule. We show that all agents can achieve the desired consensus tracking objective over the specified time interval even though the multi-agent system is subject to switching topologies. An illustrative example is included to demonstrate the effectiveness of distributed iterative learning control in multi-agent networks.

Biography

Yaonan Wang received the B.S. degree in Mathematics and Applied Mathematics from Ocean University of China (OUC), Qingdao, China, in June 2005, and the Ph.D. degree in Control Theory and Control Engineering from Beihang University (BUAA), Beijing, China, in July 2010. He is currently with the Seventh Research Division and School of Automation Science and Electrical Engineering at Beihang University. From November 2012 to November 2013, he was a visiting scholar with the Department of Electrical Engineering and Computer Science, Colorado School of Mines, Golden, CO, USA. His research interests include iterative learning control and distributed control of multi-agent networks.