Enhanced model-free adaptive iterative learning control with load disturbance and data dropout

Abstract

In this paper, an enhanced model-free adaptive iterative learning control (EMFAILC) method is proposed, which is applied for a class of nonlinear discrete-time systems with load disturbance and random data dropout. This method is a data-driven control strategy and only the I/O data are required for the controller design. Data are lost at every time instance and iteration instance independently, which allows successive data dropout both in time and iterative axes. By compensating the missing data, the proposed EMFAILC algorithm can track the desired time-varying trajectory. The convergence and effectiveness of the proposed approach are verified by both the rigorous mathematical analysis and the simulation results.

Keywords:

• Model-free adaptive control
• iterative learning control
• data dropout
• time-varying parameters

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Changchun Hua

Changchun Hua received the PhD degree in electrical engineering from Yanshan University, Qinhuangdao, China, in 2005. He was a research Fellow in National University of Singapore from 2006 to 2007. From 2007 to 2009, he worked in Carleton University, Canada, funded by Province of Ontario Ministry of Research and Innovation Program. From 2009 to 2010, he worked in University of Duisburg, Essen, Germany, funded by Alexander von Humboldt Foundation. Now he is a full Professor in Yanshan University, China. He is the author or coauthor of more than 120 papers in mathematical, technical journals, and conferences. He has been involved in more than 15 projects supported by the National Natural Science Foundation of China, the National Education Committee Foundation of China, and other important foundations. He is Cheung Kong Scholars Programme Special appointment professor. His research interests are in nonlinear control systems, multiagent systems, control systems design over network, teleoperation systems and intelligent control.

Yunfei Qiu

Yunfei Qiu received the BS degree in building electricity an intelligence from Shenyang Jianzhu University, Shenyang, China, in 2013. She is currently pursuing the PhD degree with Yanshan University, Qinhuangdao, China. Her current research interests include model free adaptive control, iterative learning control, and delay systems.

Xinping Guan

Xinping Guan received the BS degree in mathematics from Harbin Normal University, Harbin, China, and the MS degree in applied mathematics and the PhD degree in electrical engineering, both from Harbin Institute of Technology, in 1986, 1991, and 1999, respectively. He is with the Department of Automation, Shanghai Jiao Tong University. He is the (co)author of more than 200 papers in mathematical, technical journals, and conferences. As (a)an (co)-investigator, he has finished more than 20 projects supported by National Natural Science Foundation of China (NSFC), the National Education Committee Foundation of China, and other important foundations. He is Cheung Kong Scholars Programme Special appointment professor. His current research interests include networked control systems, robust control and intelligent control for complex systems and their applications. Dr Guan is serving as a Reviewer of Mathematic Review of America, a Member of the Council of Chinese Artificial Intelligence Committee, and Vice-Chairman of Automation Society of Hebei Province, China.