Adaptive Intelligent Control-Based Consensus Tracking for a Class of Switched Non-Strict Feedback Nonlinear Multi-Agent Systems With Unmodeled Dynamics

Based on adaptive intelligent control technique, we develop an adaptive consensus tracking control scheme for the distributed switched non-strict feedback nonlinear multi-agent systems (MASs) with unmodeled dynamics. According to the properties of Gaussian functions, a fresh scheme is utilized to handle the non-strict feedback form. Based on the characters of unmodeled dynamics, a dynamic signal is designed to resolve the design difficulties caused by the unmodeled dynamics. In the design process of the adaptive switching controllers, the backstepping technique is adopted and the unknown nonlinear functions are estimated by utilizing radial basis function neural networks (RBF NNs) intelligent technique. Besides, a common Lyapunov function (CLF) is constructed to analyse the system stability. Under the Lyapunov stability theory, it is rigorously demonstrated that all signals of the closed-loop switched MAS are cooperatively semi-global uniformly ultimately bounded (CSUUB) and the outputs of all the followers eventually track the leader's output. Finally, the simulation results of a practical example is presented to confirm the efficiency of the proposed control scheme.