Adaptive fault-tolerant consensus for a class of leader-following systems using neural network learning strategy. (January 2020)

• Record Type: Journal Article
• Authors: Jin, Xiaozheng; Zhao, Xianfeng; Yu, Jiguo; Wu, Xiaoming; Chi, Jing
• Is Part Of: Neural networks. Volume 121(2020)
• Page Start: 474
• Page End: 483
• Publication Date: 2020-01
• Subjects: Leader-following consensus -- Adaptive fault tolerant control -- Neural network learning strategy -- Actuator faults; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.09.028
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, the leader-following consensus problem of a class of nonlinearly multi-dimensional multi-agent systems with actuator faults is addressed by developing a novel neural network learning strategy. In order to achieve the desirable consensus results, a neural network learning algorithm composed of adaptive technique is proposed to on-line approximate the unknown nonlinear functions and estimate the unknown bounds of actuator faults. Then, on the basis of the approximations and estimations, a robust adaptive distributed fault-tolerant consensus control scheme is investigated so that the bounded results of all signals of the resulting closed-loop leader-following system can be achieved by using Lyapunov stability theorem. Finally, efficiency of the proposed adaptive neural network learning strategy-based consensus control strategies is demonstrated by a coupled nonlinear forced pendulums system.