Adaptive tracking controller using neural networks for nonlinear systems

Abstract

A neural network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that two uncertainty bounds are approximated by using RBF neural networks, and the outputs of the neural networks are then used as the parameters of controller to compensate the effects of system uncertainties. Using the this scheme, not only strong robustness with respect to unknown dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference signal can be guaranteed to asymptotically converge to zero.