Pinning exponential boundedness of fractional-order multi-agent systems with intermittent combination event-triggered protocol

ABSTRACT

This paper mainly deals with the exponential boundedness of fractional-order multi-agent systems (FOMSs). A novel controller, intermittent combination event-triggered strategy, is proposed to save more control resources. Unlike the traditional event-triggered mechanism, the combined event-triggered strategy includes the item of the error function and exponential function. The communication between agents is intermittent. Under the pinning control, only a few agents can receive information from the virtual leader. By utilising the designed controller, sufficient conditions for exponential boundedness of FOMSs are gained along with fractional-order Lyapunov methods, the monotonicity of the Mittag-Leffler function and matrix analysis. The article also investigates the consensus of FOMSs via pinning combination event-triggered control without the intermittent mechanism through the same analytical approach. The Zeno behaviour is also excluded. Simulations are given to verify the availability and applicability of the designed protocol. Experiments demonstrate that the combined event-triggered mechanism can reduce the number of event triggers.

Keywords:

• Fractional-order
• boundedness
• intermittent control
• combination event-triggered mechanism
• pinning

Disclosure statement

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

Additional information

Funding

This work was supported by the Natural Science Foundation of Jiangsu Province of China [grant numbers BK20170171 and BK20181342].

Notes on contributors

Qi Chang

Qi Chang received her B.S. degree in Information and Computing Science from Jiangnan University, Wuxi, China, in 2017. Now she is working on the Ph.D. degree at School of Internet of Things, Jiangnan University, Wuxi, China. Her current research mainly focuses on dynamic and control of memristive neural networks and finite-time synchronization.

Aihua Hu

Aihua Hu received her BS degree in information and computing science from the Jiangnan University, Wuxi, China, in 2003, and her MS degree and PhD degree in control theory and engineering from the Jiangnan University, Wuxi, China, in 2006 and 2010, respectively. From June 2012 to June 2014, she was a postdoctoral research fellow at the School of Automation, Southeast University, Nanjing, China. Currently, she is an associate professor and graduate student advisor at the Jiangnan University. She is the author or co-author of about 20 journal papers. Her research interests include non-linear systems, stochastic systems, and complex networks.

Yongqing Yang

Yongqing Yang received the B.S. degree from Anhui Normal University, Wuhu, China, the M.S. degree from Anhui University of Science and Technology, Huainan, China, and the Ph.D. degree from Southeast University, Nanjing, China, in 1985, 1992, and 2007, respectively. He is currently a professor of Jiangnan University. He is the author or coauthor of more than 30 journal papers. His research interests include nonlinear systems, neural networks and optimization.

Li Li

Li Li received the B.S. degree in Mathematics and the M.S. degree in Applied Mathematics from Anhui Normal University,Wuhu,China, the Ph.D. degree in Control Science and Engineering from Jiangnan University,Wuxi, China, in 2001, 2007 and 2016, respectively. Her main science interest is in the field of nonlinear systems, neural networks and computational intelligence.