Quantised adaptive consensus control of heterogeneous nonlinear multi-agent systems under external disturbance

Abstract

This paper is primarily devoted to the distributed consensus tracking control for heterogeneous nonlinear multi-agent systems (MASs) with a directed communication topology. Taking the considered heterogeneous nonlinear MASs own unknown nonlinear term and quantised input signals, the adaptive radial basis function neural networks (RBFNNs)-based approximator is utilised to tackle these interesting challenges. Next, considering the boundness of disturbances, the upper bound of time-varying disturbance is utilised in the controller design. Then, a distributed dynamic compensator is developed to complete coordination task, which only requires to exchange output information via the communication network. In addition, the compensator transforms the adaptive output consensus problem into asymptotic tracking problem, without needing extra stability conditions. Finally, by means of the Lyapunov stability theory, the satisfying result of the consensus error converging to zero asymptotically can be obtained. Through simulations, the effectiveness of the developed protocol is illustrated.

Keywords:

• Heterogeneous nonlinear multi-agent systems (MASs)
• adaptive control
• time-varying disturbance
• input quantisation
• distributed dynamic compensator

Disclosure statement

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

Additional information

Funding

This research was funded by the National Science Foundation of China (Grant Nos. 62373176 and 61973146), the Applied Basic Research Program of Liaoning Province (No. 2022JH2/101300276), and the Key Project of the Educational Department of Liaoning Province (Grant No. JYTZD2023084).

Notes on contributors

Chang-Chun Sun

Chang-Chun Sun received the B.S. degree in information and computing sciences from Liaoning University of Technology, China, in 2022. He is currently pursuing the M.S. degree in mathematics from the Liaoning University of Technology, Jinzhoy, China. His current research interests include adaptive control, iterative learning control and nonlinear multi-agent systems.

Jiaxu Sun

Jia-Xu Sun received the B.S. degree in measurement and control technology and instruments from Qingdao Agricultural University, China, in 2020. He received the M.S. degree in system theory from Qingdao University, Qingdao, China, in 2023. His current research interests include adaptive fuzzy control, quantisation control and MASs.

Yuan-Xin Li

Yuan-Xin Li received the B.S. degree in mathematics and applied mathematics from Qufu Normal University, China, in 2007, the M.S. degree in computational mathematics from the College of Mathematical Sciences, Dalian University of Technology, Dalian, China, in 2009, and the Ph.D. degree in control theory and control engineering from the College of Information Science and Engineering, Northeastern University, Shenyang, China, in 2017. He is currently a professor in the Department of Science, Liaoning University of Technology, Jinzhou, China. His research interests include adaptive fuzzy/neural control, fault-tolerant control, event-triggered control and adaptive control of cyber-physical systems.