Simplified optimised prescribed performance control for high-order multiagent systems with privacy preservation

Abstract

This paper studies the consensus tracking problem based on optimised backstepping technique for multiagent systems with power exponential functions and the privacy protection. Under the optimised backstepping technique, the controllers with power functions are designed by utilising the reinforcement learning strategy. Moreover, the synchronisation error converges to a predetermined region within a user-defined settling time by utilising a speed function. To ensure the safety of communication, the privacy protection is applied to multiagent systems, which effectively prevent information exposure. Based on Lyapunov stability theory, the tracking errors of the system with power exponential functions converge to a predetermined region, and a simulation example proves the feasibility of the proposed strategy.

Keywords:

• Optimised backstepping
• power exponential functions
• privacy protection
• reinforcement learning
• speed function

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was partially supported by the National Natural Science Foundation of China (62103108, 62003093), the Basic Scientific Research Project of the Education Department of Liaoning Provincial (LJKQZ20222437), the Natural Science Foundation of Guangdong Province (2022A1515011506) and the Guangzhou Science and Technology Planning Project (202102020586).

Notes on contributors

Min Wang

Min Wang received the B.S. degree in mathematics and applied mathematics from Bohai University, Jinzhou, China, in 2021. She is currently pursuing the M.S. degree in control theory and control engineering with Bohai University, Jinzhou, China.

Hongjing Liang

Hongjing Liang received the B.S. degree in mathematics from Bohai University, Jinzhou, China, in 2009, and the M.S. degree in fundamental mathematics and the Ph.D. degree in control theory and control engineering in 2016, both from Northeastern University, Shenyang, China. He was a Temporary Research Associate with the Science Program, Texas A&M University, Doha, Qatar. He is currently a Professor with the University of Electronic Science and Technology of China, Chengdu, China. His current research interests include adaptive control, fuzzy control, and cooperative control and their applications.

Liang Cao

Liang Cao received the Ph.D. degree in control science and engineering from the Guangdong University of Technology, Guangzhou, China, in 2019. From 2020 to 2022, he was a Post-Doctoral Researcher with the School of Automation, Guangdong University of Technology. He is currently working with the College of Mathematical Sciences, Bohai University, Jinzhou, China. His current research interests include event-triggered control, intelligent control, and adaptive control for nonlinear systems.

Hongru Ren

Hongru Ren received the B.E. and Ph.D. degrees in control science and engineering from the University of Science and Technology of China, Hefei, China, in 2013 and 2019, respectively. He is with the School of Automation and the Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou, China. His research interests include complex nonlinear systems, networked control systems, and unmanned autonomous systems.