Globally Stable Adaptive Neural Network Tracking Control for Uncertain Output-Feedback Systems With Prior Tracking Accuracy

This paper is concerned with the problem of globally stable adaptive neural network tracking control for a class of output feedback systems with unknown functions. Unknown functions are approximated via online radial basis function neural network, continuously differentiable functions are introduced into Lyapunov functions to realize parameter estimation. Barbalat’s lemma is used to prove that all closedloop signals are globally uniformity ultimately bounded and tracking error can reach prior accuracy. A simulation example is given to verify the effectiveness of the control method.