Adaptive Event-Triggered Fixed-Time Fault-Tolerant Consensus Control for a Class of Multiagent Systems

This article investigates an adaptive event-triggered fixed-time fault-tolerant consensus control for a category of multiagent systems (MASs). First, radial basis function (RBF) neural networks (NNs) are applied to handle unknown nonlinear terms. Furthermore, the backstepping technique is employed to construct the fixed-time event-triggered consensus control scheme by utilizing the command filter technique. The proposed scheme can guarantee the boundedness of all signals in the closed-loop system and the fixed-time convergence of consensus error. Additionally, a simulation example is presented to verify the validity of the presented results.