Efficient data-driven event-triggered predictive control for cyber-physical systems

Abstract

As a multi-dimensional complex system that integrates computing, network, and physical environments, cyber-physical system (CPS) involves a large amount of system data in its real-time control process. The efficiency of data processing and transmission in CPS plays an important role in its control performance, however, few results have addressed such problems. This paper proposes a novel data-driven event-trigger mechanism to alleviate the data processing and transmission load for the data-driven predictive control method in CPS. The design of the event-triggered law can be transformed into solving a linear matrix inequality (LMI) based on the measured system data, while ensuring the stability of the integrated system. The improved performances are illustrated by the explicit simulation results, which indicates its potential application in CPS.

Keywords:

• Cyber-physical system (CPS)
• data processing and transmission load
• event-trigger
• predictive control

Disclosure statement

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

Additional information

Funding

This work was supported in part by the National Key R&D Program of China [grant number 2022YFB4003800], in part by the National Natural Science Foundation of China [grant numbers 61903132 and 62203159], in part by the Natural Science Foundation of Hunan Province [grant numbers 2024JJ4014 and 2022JJ40097], and in part by the Major project of Xiangjiang Laboratory [grant number 22xj01006].

Notes on contributors

Zhe Li

Zhe Li received the B.S, M.S. and Ph.D. degrees in control engineering with Northeastern University, Liaoning, China, in 2011, 2013 and 2018, respectively. He is currently working as an Associate Professor at Hunan University, Chiangsha, China, and also with the National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha, China. His current research interests include data-driven control methods, intelligent fault diagnosis and industrial cyber-physical systems.

Kexin Liu

Kexin Liu received the B.S. degree from Automation, Hefei University of Technology, Xuancheng, China, in 2021. She is currently working toward the Ph.D. degree in Control Science and Engineering, Hunan University, Changsha, China. Her research interests include data-driven predictive control and fault diagnosis.

Xudong Wang

Xudong Wang received the B.S. and Ph.D. degrees in Control Science and Engineering from Harbin Institute of Technology, Harbin, China, in 2016 and 2021, respectively. He is currently an Assistant Professor with the School of Robotics, Hunan University, Changsha, China, and also with the National Engineering Research Center of Robot Visual Perception and Control Technology, Changsha, China. His current research interests include fault diagnosis, fault-tolerant control, event-triggering control, and cyber-physical systems

Xiaofang Yuan

Xiaofang Yuan received the B.S., M.S., and Ph.D. degrees in electrical engineering from Hunan University, Changsha, China, in 2001, 2006, and 2008, respectively. He is currently a Professor with Hunan University. His research interests include intelligent control theory and application, industrial process control, and artificial neural networks.

Yaonan Wang

Yaonan Wang received the B.S. degree in computer engineering from East China Science and Technology University, Fuzhou, China, in 1981, and the M.S. and Ph.D. degrees in electrical engineering from Hunan University, Changsha, China, in 1990 and 1994, respectively. He was a Senior Humboldt Fellow in Germany from 1998 to 2000 and a Visiting Professor with the University of Bremen, Bremen, Germany, from 2001 to 2004. He is currently the Director of the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, a Professor and a Supervisor of doctoral students at the College of Electrical and Information Engineering, Hunan University, and a member of the Chinese Academy of Engineering, Beijing, China. His research efforts mainly to robotics, intelligent information processing, intelligent control and pattern recognition.