Distributed dynamic event-triggered algorithm with minimum inter-event time for multi-agent convex optimisation

Abstract

In this paper, the distributed convex optimisation problem of the multi-agent system over an undirected network is investigated, in which the local objective function of each agent is only known by itself. To reduce the communication consumption between agents, a state-based dynamic event-triggered algorithm with positive minimum inter-event time (MIET) is provided, where the aperiodic information communication only occurs at some discrete triggering time instants. Moreover, the sampling control technology is combined into the previous event-triggered algorithm for verifying the event-triggered condition at every sampling time, instead of continuous access. Finally, several numerical simulations are presented for illustrating and verifying the proposed algorithms.

Keywords:

• Distributed convex optimisation
• event-triggered
• sampling control
• minimum inter-event time

Acknowledgments

The work was supported by the National Natural Science Foundation of China (nos. 61673344, 61976215, 61991403 and 61991400).

Disclosure statement

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

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [grant numbers 61673344, 61976215, 61991403 and 61991400].

Notes on contributors

Xiasheng Shi

Xiasheng Shi received his Ph.D. in control theory and control engineering in the College of Electrical Engineering, Zhejiang University, China in 2020. He is currently a lecturer in the School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China. His research interests lie in the area of distributed economic dispatch problem and its application.

Zhiyun Lin

Zhiyun Lin received his Ph.D. in electrical and computer engineering from the University of Toronto, ON, Canada, in 2005. He is currently a professor in the School of Automation, Hangzhou Dianzi University, Hangzhou, China. His research interests include distributed control, estimation and optimization, cooperative control of multiagent systems, hybrid control system theory, and robotics.

Tao Yang

Tao Yang received his Ph.D. in electrical engineering from Washington State University, Pullman, WA, USA, in 2012. He is currently a professor with the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China. His research interests include distributed control and optimization with applications to power systems, cyber-physical systems, networked control systems, and multiagent systems.

Xuesong Wang

Xuesong Wang received the Ph.D. degree in control science and technology from the China University of Mining and Technology, Xuzhou, China, in 2002., She is currently the Dean of the Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, the Dean of Xuzhou Key Laboratory of Artificial Intelligence and Big Data, and a Professor with the School of Information and Control Engineering, China University of Mining and Technology. Her main research interests include machine learning, bioinformatics, and artificial intelligence., Prof. Wang is currently an Associate Editor for the IEEE Transactions on Systems, Man, and Cybernetics: Systems and International Journal of Machine Learning and Cybernetics.