Adaptive NN control for a class of strict-feedback discrete-time nonlinear systems via backstepping

Abstract

In this paper, the state feedback controller is studied for a class of strict-feedback discrete-time nonlinear systems in the presence of bounded disturbances. A Lyapunov-based full state feedback neural network control structure is presented via backstepping, which solves the noncausal problem in the discrete-time back-stepping design procedure. The closed-loop system is proven to be semi-globally uniformly ultimately bounded (SGUUB). An arbitrarily small tracking error can be achieved if the size of the neural network is chosen large enough, and the control performance of the closed-loop system is guaranteed by suitably choosing the design parameters.