Distributed adaptive output-feedback fault tolerant control for nonlinear systems with sensor faults

Abstract

 This paper studies the distributed adaptive output-feedback fault tolerant control problem for leader-following multiagent systems with sensor faults. By using the approximation theory of neural networks, an unknown continuous function is approximated, and the problem that the neural network output deviates from the true approximation of the unknown function due to the faults of neural network input is solved. A filter observer is adopted to estimate the unmeasurable states. Based on the adaptive backstepping technique and fault tolerant control technique, a distributed adaptive neural output-feedback control scheme is proposed to guarantee the output consensus of all nodes under directed communication graphs. Based on graph theory and Lyapunov stability theory, it is proved that the proposed adaptive neural control scheme guarantees the uniformly ultimate boundness of the closed-loop systems, and the tracking errors converge to a small adjustable neighborhood of the origin. The simulation results demonstrate the effectiveness of the control approach in this paper.