讲座题目：Feedback limitations in nonlinear discrete-time control
An interesting phenomenon occurs when one attempts to control systems with output nonlinearity growing faster than linearity, where similarities between the continuous- and discrete-time cases of adaptive control no longer exist. It is generally known that a large class of continuous-time nonlinear parametric systems, regardless of how fast the growth rate is, can be globally stabilized by the nonlinear damping or back-stepping approach in adaptive control. However, fundamental difﬁculties arise for the discrete-time case. These difﬁculties are caused by the inherent limitations of the feedback principle in dealing with uncertainties, which means that systems with uncertainties beyond the feedback capability cannot be stabilized by any discrete-time feedback control law. This talk studies the stabilizability of discrete-time nonlinear parametric systems and tries to give an appropriate characterization of feedback limitations.
Chanying Li received the B.S. degree in Mathematics from Sichuan University, and the M.S. and Ph.D. degrees in control theory from Academy of Mathematics and Systems Science, Chinese Academy of Sciences, respectively. After receiving the Ph.D. degree, she held postdoctoral positions at the Wayne State University and the University of Hong Kong. In November 2011, she joined the Faculty of the Institute of Systems Science at Chinese Academy of Sciences, where she is currently a professor. Her research interests include the maximum capability of feedback, adaptive nonlinear control and system identification.
Prof. Li has received the National Science Fund for Distinguished Young Scholars and the National Science Fund for Excellent Young Scholars, respectively. She was also the recipient of the Young Scholar Prize by China Society for Industrial and Applied Mathematics in 2018 and Guan Zhao-Zhi Award of the 33th Chinese Control Conference in 2014. She is an Associate Editor of Automatica, Mathematical Control and Related Fields, and Journal of Systems Science and Complexity.