Adaptive Consensus Tracking Control for a class of Switched Nonlinear Non-strict Feedback Multi-agent Systems with Unmodeled Dynamics

This paper investigates the adaptive consensus tracking control problem for a class of switched nonlinear non-strict feedback multi-agent systems (MASs) with unmodeled dynamics. The difficulties of controller design are caused by the arbitrarily switched process and the completely unknown nonlinearities in the switched MASs. In order to deal with the difficulties, a new control strategy is constructed by combining radial basis function neural networks (RBF NNs) with adaptive backstepping method. It is theoretically proved that the proposed control strategy not only guarantees all signals of the MASs are bounded but also preserves that all the followers' outputs can eventually be synchronized with the leaders. In the end, the effectiveness of the control protocol has been verified by simulation results.