Observer-Based Adaptive Neural Networks Control of Nonlinear Pure Feedback Systems with Hysteresis

Abstract

In this paper, the problem of adaptive neural output feedback control is investigated for a class of uncertain nonlinear pure feedback systems with unknown backlash-like hysteresis. In the design, RBF neural networks are used to approximate the nonlinear functions of systems, and a neural state observer is designed to estimate the unmeasured states. By utilizing the neural state observer, and combining the backstepping technique with adaptive control design, an observer-based adaptive neural output feedback control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SUUB), and both observer error and tracking error can converge to a small neighborhood of the origin.