Title: Adaptive Iterative Learning Control for Nonlinear Systems 

Abstract:  As a complementary method to traditional iterative learning control (ILC), adaptive ILC has been developed for more than 20 years and has achieved many important research results. In this talk, we will first introduce the developments of adaptive ILC, and then summarize the main difficulties and challenges we encounter in this area from both theoretical and practical aspects. In view of these difficulties, we will introduce some of our research progresses, including adaptive ILC for systems with unmeasurable states, adaptive ILC for underactuated nonlinear systems and its applications in autonomous driving. The proposed approaches provide new adaptive ILC design frameworks to break the original limitations, which might be able to refine the adaptive ILC theory and to widen its application scope. 

Biography: Xuefang Li, Associate Professor of the School of Intelligent Systems Engineering of Sun Yat-sen University. She received the B.Sc. and M.Sc. degrees from the Mathematical College, Sichuan University, Chengdu, China, in 2009 and 2012, respectively, and her PhD degree from the Department of Electrical and Computer Engineering, National University of Singapore in 2016. From 2016 to 2019, she was a Research Associate with the Department of Electrical and Electronic Engineering (EEE), Imperial College London, London, U.K. She was awarded by several best conference paper awards including IEEE 9th DDCLS, IEEE 13th ICCAS, 10th ASCC. She has published over 50 research papers and coauthored two research monographs. Her research interests include that learning and adaptive control theory as well as their applications to robotics, new energy vehicles and intelligent vehicles.