Distributed adaptive fault-tolerant control of pure-feedback nonlinear multi-agent systems with actuator failures

In this paper, we are concerned with the output tracking consensus matter for a class of pure-feedback nonlinear multi-agent systems (MASs) with actuator failures comprising loss of effectiveness and bias. Taking advantage of relative information from individual agents and their neighbors, a distributed adaptive fault-tolerant control strategy is proposed recursively by the approximation property of neural networks (NNs), backstepping methods, dynamic surface control (DSC) methodology and algebraic graph theory. The distinct features of this control approach are that it is not of requirement of prior information of individual agents with the help of approximation property, and the online update parameters are the norms of NNs weight vectors instead of weight vectors themselves. Also, it reduces the computational burden considerably by introducing the DSC approach. The stability of the resulting closed-loop system is rigorously investigated and it is proven that the consensus tracking errors of the MASs under directed communication topology converge to a small adjustable neighborhood around the origin in spite of actuator failures. Two simulation examples, both practical and numerical ones, are presented to verify the effectiveness of the proposed approach.