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

In this paper, a neural-network-based control scheme is developed for the tracking control problem of a class of disturbed nonlinear switched strict-feedback systems with input delay. First, the auxiliary signals are obtained by ingeniously constructing a filter and a virtual observer. Then the backstepping technique and neural networks are employed to construct a common Lyapunov function (CLF) and a state feedback controller for all subsystems. It is proved all signals of the closedloop system are semi-globally uniformly ultimately bounded (SGUUB), and that the tracking error ultimately converges to an adequately small compact set.