An open-closed-loop iterative learning control approach for nonlinear switched systems with application to freeway traffic control

ABSTRACT

For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law.

KEYWORDS:

• Freeway traffic
• iterative learning control (ILC)
• input constraints
• nonlinear switched discrete-time systems

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported in part by the National Natural Science Foundation of China [grant number 61573385], [grant number 61672546].

Notes on contributors

Shu-Ting Sun

Shu-Ting Sun received the B. S. degree from the Department of Electronics and Science, Huizhou University, Huizhou, China, in 2014, the M. Phil. degree from the School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou, China, in 2017. At present, she is pursuing her PhD degree from the School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China. Her research interests include switched system theory and iterative learning control.

Xiao-Dong Li

Xiao-Dong Li received the B. S. degree from the Department of Mathematics, Shaanxi Normal University, Xian, China, in 1987, the M. Phil. degree from the Nanjing University of Science and Technology, Nanjing, China, in 1990, and the PhD degree from the City University of Hong Kong, Hong Kong, in 2007. He is currently a Professor in the School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China. His research interests include two-dimensional system theory, iterative learning control, and artificial intelligence.

Ren-Xin Zhong

Ren-Xin Zhong received the B. Eng in Electronic Engineering from Sun Yat-sen University, Guangzhou, China, in 2005, the M.Phil in Automatic Control Engineering from Chinese University of Hong Kong, Hong Kong, in 2007, and the Ph.D. degree in Transportation Engineering from Hong Kong Polytechnic University, Hong Kong, in 2011. He joined the Research Center of Intelligent Transportation Systems of Sun Yat-sen University as an Associate Professor in 2012. His main research interests include dynamic traffic surveillance and assignment, traffic incident detection and management strategies, machine learning and data mining for transportation big data analysis, optimal and nonlinear control theory with applications in transportation engineering.