Adaptive Neural Network Output-Feedback Control for a Class of Discrete-Time Nonlinear Systems in Presence of Input Saturation

摘要

In this paper, an adaptive neural network output-feedback control approach is presented for a class of discrete-time nonlinear strict-feedback systems in presence of input saturation. An auxiliary design system is employed to overcome the problem of input saturation constraint, and states of auxiliary design system are utilized to develop the tracking control. The high-order neural network (HONN) is employed to approximate unknown function. It is shown via Lyapunov theory that all the signals in closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the control parameters appropriately. A simulation example is included to illustrate the effectiveness of the proposed approach.