MLP-based adaptive neural control of nonlinear time-delay systems with the unknown hysteresis

ABSTRACT

In this note, the authors study the tracking problem for uncertain nonlinear time-delay systems with unknown non-smooth hysteresis described by the generalised Prandtl–Ishlinskii (P-I) model. A minimal learning parameters (MLP)-based adaptive neural algorithm is developed by fusion of the Lyapunov–Krasovskii functional, dynamic surface control technique and MLP approach without constructing a hysteresis inverse. Unlike the existing results, the main innovation can be summarised as that the proposed algorithm requires less knowledge of the plant and independent of the P-I hysteresis operator, i.e. the hysteresis effect is unknown for the control design. Thus, the outstanding advantage of the corresponding scheme is that the control law is with a concise form and easy to implement in practice due to less computational burden. The proposed controller guarantees that the tracking error converges to a small neighbourhood of zero and all states of the closed-loop system are stabilised. A simulation example demonstrates the effectiveness of the proposed scheme.

KEYWORDS:

• Adaptive neural control
• backstepping
• hysteresis
• time delays
• nonlinear system

Acknowledgment

The authors would like to thank anonymous reviewers for their valuable comments to improve the quality of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is partially supported by the National Postdoctoral Program for Innovative Talents [grant number BX201600103]; the China Postdoctoral Science Foundation [grant number 2016M601600]; the National Natural Science Foundation of China [grant number 61521063], [grant number U1509211], [grant number 51409033]; the Program of Shanghai Subject Chief Scientist [grant number 14XD1402400]; the Fundamental Research Funds for the Central University [grant number 3132016001].

Notes on contributors

Guoqing Zhang

Guoqing Zhang received his B.Sc. and Ph.D. degrees from the Navigation College, Dalian Maritime University (DMU), Dalian, China, in 2010 and 2015. Now, he is a lecture of DMU and working as a Postdoctoral Fellow at Shanghai Jiao Tong University. He is a recipient of National Postdoctoral Innovative Talent Scholars of China, and has received the National Excellent Doctoral Dissertation Award in the field of Intelligent Transportation. His current research interests include adaptive control, nonlinear control and their application on the marine control system.

Zhijian Sun

Zhijian Sunreceived his B.S. degree from Harbin Institute of Technology, Harbin, China, in 2011 and M.S. degree from Harbin Engineering University, Harbin, China, in 2015, respectively. He is now working toward a Ph.D. degree in Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China. His current research interests include Dynamic positioning system, Sliding mode control, and Navigation guidance and control.

Weidong Zhang

Weidong Zhangreceived his B.S., M.S., and Ph.D. degrees from Zhejiang University, China, in 1990, 1993, and 1996, respectively, and then worked as a Postdoctoral Fellow at Shanghai Jiao Tong University. He joined Shanghai Jiao Tong University in 1998 as an Associate Proffessor and has been a Full Professor since 1999. From 2003 to 2004, he worked at the University of Stuttgart, Germany, as an Alexander von Humboldt Fellow. In 2011, he was appointed Chair Professor at Shanghai Jiao Tong University. Currently, he is the Director of Shanghai Engineering Research Center of Marine Automation and the Deputy Dean of the Departments include control theory and its applications in industry. He is the author of more than 200 refereed papers and 1 book, and holds 26 patents. He is a recipient of National Science Fund for Distinguished Young Scholars of China.

Lei Qiao

Lei Qiaoreceived his B.S. and M.Eng. degrees from the College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China, in 2012 and 2014, respectively. Since 2014, he has been working toward the Ph.D. degree in the Department of Automation, Shanghai Jiao Tong University, Shanghai, China. His research interests include sliding mode control, nonlinear control, intelligent control, and their applications to autonomous marine vehicles.