Globally Stable Adaptive Neural Network Control for Uncertain Output-Feedback Systems

This paper investigates the problem of global adaptive neural network tracking control for uncertain output feedback systems under disturbances with unknown bounds. The designed actual controller consists of neural network controller working in the approximate domain and robust controller working outside the approximate domain, in addition, a function is designed to realize the switching between the two, so as to ensure the globally uniformly ultimately bounded (GUUB) of all closed-loop signals, and the output tracking error is guaranteed to converge to a neighborhood. A simulation example is provided to verify the effectiveness of the proposed control method.