Fixed-time adaptive neural tracking control for nonlinear multiagent systems with communication link faults

This paper addresses the fixed-time adaptive neural distributed control problem of uncertain nonlinear multiagent systems (MASs) with unknown communication link faults. To handle the unknown terms caused by communication link faults and the unknown functions in each agent, a radial basis function neural network (RBF NN) is employed. In contrast to existing methods that focus solely on practical finite-/ fixed-time stability of nonlinear systems, the control scheme developed in this paper goes beyond by not only mitigating the effects of unknown communication link faults in nonlinear MASs but also addressing the singularity problem of fixed-time controllers. These advancements are realized through the design of new power exponent functions and the introduction of new lemmas. This way, the tracking error of MASs converges to a preset range within a fixed time and achieves better control performance. Finally, the feasibility of the proposed scheme is verified through two simulations.