The safety region-based model predictive control for discrete-time systems under deception attacks

Abstract

In this paper, a resilient control problem is investigated for the discrete-time linear system subjected to both input constraint and additive disturbances. In addition, deception attacks are also taken into consideration which could compromise the communication channels. To deal with the mentioned complexities, the so-called safety region-based defense strategy (SRDS) is designed in the context of the robust model predictive control (RMPC) method. The original constraints of the system are used to establish the safety region. Sufficient conditions are established for the proposed SRDS such that the recursive feasibility and stability of the overall closed-system are guaranteed. Finally, two numerical examples are conducted to verify the effectiveness of the proposed methodology.

Keywords:

• Resilient control
• deception attacks
• safety region-based defense strategy (SRDS)
• model predictive control (MPC)
• cyber-physical systems (CPSs)

Acknowledgments

The authors gratefully acknowledge the reviewers and the associate editor for their valuable comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Yang Xu

Yang Xu received the B.S. degree in Electrical engineering from Hebei University of Science and Technology, Shijiazhuang, China, in 2015, and the M.S. degree in the Pattern recognition and intelligent system, Yanshan University, China, in 2018. He is currently working toward the Ph.D. degree in aircraft design with School of Astronautics, Northwestern Polytechnical University, China.

Yuan Yuan

Yuan Yuan was born in Xi'an, Shaanxi Province, China in 1986. He received his B.Sc degree in the School of Instrumental Science and Opto-electronics Engineering, Beihang University, Beijing, China in 2009 and the Ph.D degree in computer science and technology from Department of Computer Science and Technology, Tsinghua University, Beijing, China in 2015.

Hongjiu Yang

Hongjiu Yang received the B.S. degree in mathematics and applied mathematics and the M.S. degree in applied mathematics from Hebei University of Science and Technology, Shijiazhuang, China, in 2005 and 2008, respectively. He received the Ph.D. degree in control science and engineering in Beijing Institute of Technology, Beijing, China. He is currently a professor with the School of Electrical and Information Engineering, Tianjin University, China. His main research interests include robust control/filter theory, delta operator systems, networked control systems and active disturbance rejection control.

Dalin Zhou

Dalin Zhou received the B.S. degree in automation from the University of Science and Technology of China, Hefei, China, in 2012, and the Ph.D. degree in computing from the University of Portsmouth, Portsmouth, U.K., in 2019. He is currently a Senior Lecturer with the School of Computing, the University of Portsmouth, UK. His current research interests include wearable sensing and analysis, assistive robotics, rehabilitation intelligence, human behaviour analysis, human machine interaction and multimodal sensor fusion. His current research contributes to the monitoring and rehabilitation of human motor function, improving the daily life activity and working capability for both the disadvantaged group of limb-impaired patients and the aging community using multimodal sensing technology of electromyography and ultrasound, computational intelligence and machine learning algorithms for physiological signal analysis.