Neural-Network-Based Event-Triggered Adaptive Containment Control for Switched Nonlinear Multi-agent Systems

This article is related to the event-triggered adaptive containment control problem for switched nonlinear multi-agent systems (MASs), in which the nonlinear functions are completely unknown. Under this difficulty, radial basis function neural networks (RBF NNs) are used to approximate the unknown nonlinear functions. Besides, an adaptive containment control scheme with the relative threshold event-triggered control (ETC) mechanism is constructed, which can alleviate the communication burden. Based on Lyapunov stability theory, it is shown that all the signals in the closed-loop MASs are semi-globally uniformly ultimately bounded (SGUUB). Meanwhile, based on the proposed adaptive containment control strategy, it can ensure that all the outputs of the followers eventually enter the convex set spanned by the leaders’. Finally, the simulation results are presented to illustrate the availability of control protocol.