Finite-time fault tolerant neural control of nonlinear multi-agent systems under switching topologies

Abstract

The neural tracking control problem for nonlinear strict-feedback multi-agent systems (MASs) subject to actuator faults in finite-time is considered, where each agent communicates with its neighbours on a time-varying directed topology. Radial basis function neural networks (RBFNNs) are used to approximate the unknown internal dynamics of system. By utilising backstepping method and dynamic surface control (DSC) technique, a novel adaptive fault tolerant controller is put forward. It turns out that the designed controller can compensate the effect of uncertainties and actuator faults. Meanwhile, by constructing piecewise Lyapunov functions, sufficient conditions for dwell time of topologies are given such that system achieves finite-time tracking consensus under the designed controller. Finally, an example is carried out to verify feasibility of the theoretical result.