Predefined-time bipartite tracking consensus for second-order multi-agent systems with cooperative and antagonistic networks

Abstract

This paper addresses the predefined-time bipartite tracking problem for second-order Multi-Agent Systems (MASs) with undirected signed topologies. A group of observers, which can estimate the state tracking errors for each follower in a pre-specified time, is proposed based on the time-varying function. In order to deal with the uncertainties caused by the unknown disturbances and the unknown input signal of the leader, we propose a predefined-time distributed control protocol based on the sliding mode control method. In addition, an auxiliary dynamic sliding variable is designed to reduce system chattering. We theoretically prove that the two control protocols can drive the state trajectories of each follower to reach the corresponding sliding surface within a specified time, and finally ensure that the prescribed-time bipartite tracking consensus is achieved for the MASs. Simulations are provided to verify the proposed schemes, and the simulation results further confirm the superiority of the adaptive control protocol.

Keywords:

• Predefined-time tracking consensus
• bipartite consensus
• multi-agent systems
• prescribed-time observers
• adaptive dynamic sliding mode control

Disclosure statement

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

Additional information

Funding

This work was partially supported by the National Natural Science Foundation of China [grant number 61705127].

Notes on contributors

Yuanhong Ren

Yuanhong Ren received the M.S. degree in Computer Application Technology from the Wuhan University of Technology in 2007, and received a Ph.D. degree in Control Science and Engineering from Donghua University in 2021. She is currently a lecturer at Shanghai Urban Construction Vocational College. Her current research interests include the analysis and control of multi-agent systems and complex dynamic networks.

Zhiwen Chen

Zhiwen Chen received the M.S. degree and the Ph.D. degree from the University of Shanghai for Science and Technology in 2000 and 2011, respectively. He is currently a Professor in Shanghai Urban Construction Vocational College. His current research interests include the application and analysis of artificial intelligence technology.

Yong Ji

Yong Ji received the M.S. degree from GuangXi University in 2006. He is currently an Associate Professor at Shanghai Urban Construction Vocational College. His research interests include automation technology and application of intelligent control technology.

Zhiwei Li

Zhiwei Li received the M.S. degree in Electronic Science and Technology from Wuhan University, in 2006, and the Ph.D. degree from the Huazhong University of Science and Technology, in 2016. He is currently an Associate Professor with the Shanghai University of Engineering Science. His research interests include intelligent theory and control, image processing, and optoelectronics.