Learning control for discrete-time nonlinear systems with sensor saturation and measurement noises

ABSTRACT

The iterative learning control (ILC) is investigated for a class of nonlinear systems with measurement noises where the output is subject to sensor saturation. An ILC algorithm is introduced based on the measured output information rather than the actual output signal. A decreasing sequence is also incorporated into the learning algorithm to ensure a stable convergence under stochastic noises. It is strictly proved with the help of the stochastic approximation technique that the input sequence converges to the desired input almost surely along the iteration axis. Illustrative simulations are exploited to verify the effectiveness of the proposed algorithm.

KEYWORDS:

• Affine nonlinear systems
• sensor saturation
• iterative learning control
• stochastic approximation

Additional information

Funding

National Natural Science Foundation of China [grant number 61304085], [grant number 61673045]; Natural Science Foundation of Beijing Municipality [grant number 4152040].

Notes on contributors

Dong Shen

Dong Shen received the B.S. degree in mathematics from Shandong University, Jinan, China, in 2005. He received the Ph.D. degree in mathematics from the Academy of Mathematics and System Science, Chinese Academy of Sciences (CAS), Beijing, China, in 2010. From 2010 to 2012, he was a Post-Doctoral Fellow with the Institute of Automation, CAS. From 2016 to 2017, he was a visiting scholar at National University of Singapore, Singapore. Since 2012, he has been an associate professor with College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China. His current research interests include iterative learning controls, stochastic control and optimization. He has published more than 60 refereed journal and conference papers. He is the author of Stochastic Iterative Learning Control (Science Press, 2016, in Chinese) and co-author of Iterative Learning Control for Multi-Agent Systems Coordination (Wiley, 2017). Dr. Shen received IEEE CSS Beijing Chapter Young Author Prize in 2014 and Wentsun Wu Artificial Intelligence Science and Technology Progress Award in 2012.

Chao Zhang

Chao Zhang received the B.E. degree in automation from Beijing University of Chemical Technology, Beijing, China, in 2016. Now he is pursuing a master degree at Beijing University of Chemical Technology. His research interests include iterative learning control and its applications on motion robots.