Neural Network Output-Feedback Consensus Fault-Tolerant Control for Nonlinear Multiagent Systems With Intermittent Actuator Faults

In this article, the distributed adaptive neural network (NN) consensus fault-tolerant control (FTC) problem is studied for nonstrict-feedback nonlinear multiagent systems (NMASs) subjected to intermittent actuator faults. The NNs are applied to approximate nonlinear functions, and a NN state-observer is developed to estimate the unmeasured states. Then, to compensate for the influence of intermittent actuator faults, a novel distributed output-feedback adaptive FTC is then designed by co-designing the last virtual controller, and the problem of ``algebraic-loop'' can be solved. The stability of the closed-loop system is proven by using the Lyapunov theory. Finally, the effectiveness of the proposed FTC approach is validated by numerical and practical examples.

Automated summary

This article studies the fault-tolerant control problem for nonstrict-feedback nonlinear multiagent systems using distributed adaptive neural networks. The networks approximate nonlinear functions and a state-observer estimates unmeasured states. A novel distributed output-feedback adaptive FTC is designed to compensate for intermittent actuator faults and solve the "algebraic-loop" problem. The stability of the closed-loop system is proven using Lyapunov theory, and the effectiveness of the proposed approach is validated through numerical and practical examples.