Predefined-time adaptive neural dynamic surface tracking control for high-order nonlinear switched systems

In this article, the issue of adaptive predefined-time control for high-order switched systems is researched. Neural networks (NNs) are introduced to approximate the uncertain nonlinear functions. In particular, a novel predefined-time convergence filter is proposed to refrain from the problem of repeated differentiation of virtual controllers. On the basis of the backstepping recursion technique and the common Lyapunov function (CLF) approach, a neural adaptive predefined-time dynamic surface control (DSC) scheme is proposed that can demonstrate all the signals in closed-loop systems are bounded and the tracking error can converge to a small area near zero within predefined time. The simulation results illustrate the effectiveness of the proposed control scheme.