A survey on sliding mode control for networked control systems

Abstract

In the framework of the networked control systems (NCSs), the components are connected with each other over a shared band-limited network. The merits of NCSs include easy extensibility, resource sharing, high reliability and so forth. However, the insertion of the communication network brings many challenges, such as network-induced phenomena and cyber-security, which should be handled properly. On the other hand, the sliding mode control (SMC) has become an effective scheme for the synthesis of NCSs due to its strong robustness and SMC has wide applications in NCSs. In this paper, some recent advances on SMC for NCSs are reviewed. In particular, some new SMC schemes for NCSs subject to time-delay, packet losses, quantisation and uncertainty/disturbance are summarised firstly. Subsequently, the problem of SMC for NCSs under scheduling protocols is discussed, where different communication protocols are introduced for the energy saving purpose during the synthesis of NCSs. Next, some recent results on SMC for NCSs with actuator/sensor fault and cyber-attack are recalled. Finally, the conclusion is provided and the potential research challenges on SMC for NCSs are pointed out.

Keywords:

• Sliding mode control
• networked control systems
• network-induced phenomena
• scheduling protocols
• cyber-security

Acknowledgments

The authors would like to express their appreciations to the anonymous reviewers and Associate Editor for their helpful comments/suggestions that improved the presentation of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This paper is supported by the National Natural Science Foundation of China [grant number 61673141], [grant number 12071102]; the Outstanding Youth Science Foundation of Heilongjiang Province of China [grant number JC2018001]; the Fundamental Research Foundation for Universities of Heilongjiang Province of China [grant number 2019-KYYWF-0215]; the European Regional Development Fund and Sêr Cymru Fellowship [grant number 80761-USW-059], and the Alexander von Humboldt Foundation of Germany.

Notes on contributors

Jun Hu

Jun Hu received the B.Sc. degree in Information and Computation Science and M.Sc. degree in Applied Mathematics from Harbin University of Science and Technology, Harbin, China, in 2006 and 2009, respectively, and the Ph.D. degree in Control Science and Engineering from Harbin Institute of Technology, Harbin, China, in 2013. From September 2010 to September 2012, he was a Visiting Ph.D. Student in the Department of Information Systems and Computing, Brunel University, U.K. From May 2014 to April 2016, he was an Alexander von Humboldt research fellow at the University of Kaiserslautern, Kaiserslautern, Germany. He is a Professor and Ph.D. supervisor in the Department of Mathematics, Harbin University of Science and Technology, Harbin 150080, China. His research interests include nonlinear control, filtering and fault estimation, time-varying systems and complex networks. He has published more than 70 papers in refereed international journals. Prof. Hu serves as a reviewer for Mathematical Reviews, as an editor for Neurocomputing, Journal of Intelligent and Fuzzy Systems, Neural Processing Letters, Systems Science and Control Engineering, and as a guest editor for International Journal of General Systems and Information Fusion.

Hongxu Zhang

Hongxu Zhang received the B.Sc. degree in Information and Computation Science and M.Sc. degree in Mathematics from Harbin University of Science and Technology, Harbin, China, in 2014 and 2017, respectively. He is currently pursuing the Ph.D. degree at School of Measurement and Communication, Harbin University of Science and Technology, Harbin, China. From December 2019 to December 2020, he was a Visiting Ph.D. Student in the School of Engineering, University of South Wales, Pontypridd CF37 1DL, U.K. His research interests include optimal state estimation and sliding mode control. He is an active reviewer for many international journals.

Hongjian Liu

Hongjian Liu received the B.Sc. degree in Applied Mathematics in 2003 from Anhui University, Hefei, China and the M.Sc. degree in Detection Technology and Automation Equipments in 2009 from Anhui Polytechnic University, Wuhu, China, and the Ph.D. degree in Control Theory and Control Engineering in 2018 from Donghua University, Shanghai, China. He is currently a Professor in the School of Mathematics and Physics, Anhui Polytechnic University, Wuhu, China. His current research interests include filtering theory, memristive neural networks and network communication systems. He is a very active reviewer for many international journals.

Xiaoyang Yu

Xiaoyang Yu received his B. Sc. degree in 1984 from Harbin Institute of Electrical Engineering, received his M.Sc. degree in 1989 and Ph.D. degree in 1998 both from Harbin Institute of Technology, now he is a professor and Ph.D. supervisor in Harbin University of Science and Technology. His main research interests include vision measurement and image processing.