LAUSR

This study focuses on the problem of neural network (NN)-based tracking control for a class of uncertain multiple-input multiple-output non-linear systems in pure feedback form. An observer based on K-filters, is introduced to estimate immeasurable states. In this method, a priori knowledge of the control gain sign is relaxed by using Nussbaum-type technique. NNs are employed to approximate the unknown non-linear functions and an adaptive neural output feedback controller is constructed via backstepping technique. The Lyapunov theorem is applied to prove that the overall closed-loop adaptive control scheme is semi-globally uniformly ultimately bounded. Finally, simulation results are provided to illustrate the design procedure.