Continuous time nonlinear adaptive control based on linearization using neural network

The aim of this paper is to present a novel direct adaptive controller structure consisting of the NN and the robust adaptive controller. The functional-link network (FLN) is utilized as the NN. We show some simple learning rules for adjusting the weights of the FLN. We will also give a new stability-guaranteed adaptive algorithm for adjusting the adaptive controller parameters and the weights of the FLN by treating the total system unifying the MRAC and the FLN. It will be shown that adequate cooperation of the NN with the MRAC will improve the convergence of the adaptation of the controller parameters and the weights of the FLN. Finally, numerical simulation results will be given to examine the effectiveness of the proposed schemes.