Adaptive fault-tolerant consensus for a class of leader-following systems using neural network learning strategy

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.