AN ADAPTIVE TRACKING CONTROLLER USING NEURAL NETWORKS FOR A CLASS OF NONLINEAR-SYSTEMS

A neural-network-based adaptive tracking control scheme is proposed fo r a class of nonlinear systems in this paper. It is shown that RBF neu ral networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then u sed as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained , but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero. A simula tion example is performed in support of the proposed neural control sc heme.