Predefined-time consensus tracking of nonlinear multi-agent systems with time-varying actuator failures

This paper investigates the problem of predefined-time consensus tracking for nonlinear multi-agent systems (MASs) subject to time-varying actuator faults. First, unknown nonlinear functions are approximated using neural networks (NNs). Second, guided by the practical predefined-time stability (PPTS) criterion, a newly designed nonlinear command filter eliminates the ‘explosion of complexity’ problem. Furthermore, a predefined-time strategy is proposed to attenuate the detrimental impacts of time-varying actuator faults. The proposed scheme guarantees that all closed-loop signals remain ultimately bounded and converge to a bounded neighbourhood within the predefined time. Moreover, each follower can track the leader's desired trajectory. Finally, simulation results demonstrate the effectiveness of the developed method.