Event-triggering based adaptive neural tracking control for a class of pure-feedback systems with finite-time prescribed performance

Abstract In this paper, a finite-time tracking control problem is considered for a class of pure-feedback nonlinear systems with event-triggered strategy. The implicit function theorem and the mean value theorem are used to transform the pure-feedback nonlinear systems into strict feedback nonlinear systems. The neural network is adopted to approximate the unknown function and the tracking error is limited to a pre-given boundary by prescribed performance at a finite time. In addition, an improved event-triggered control strategy is proposed to obtain a larger threshold, and also the proposed controller can avoid the Zeno-behavior. Based on Lyapunov stability theory, the adaptive neural network controller can ensure that all the signals in the closed-loop system are uniformly ultimately bounded. Finally, the feasibility of this control scheme is proved by simulation.