Adaptive neural network tracking control for a class of switched strict-feedback nonlinear systems with input delay

Abstract

In this paper, a neural-network-based control scheme is developed for the tracking control problem of a class of switched strict-feedback nonlinear systems with uncertain input delay and external time-varying disturbances. First, the auxiliary signals are obtained by masterly constructing a filter and a virtual observer. Then the adaptive backstepping technique and neural network (NN) are employed to construct a common Lyapunov function (CLF) and a state feedback controller for all subsystems. It is proved that all signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB), and that the tracking error ultimately converges to an adequately small compact set. Finally, a simulation example is given to illustrate the effectiveness of the proposed control approach.