Output feedback control of nonlinear systems using RBF neural networks

An adaptive output feedback control scheme is presented for output tracking of a class of continuous-time nonlinear plants. An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design. The method uses parameter projection, control saturation, and a high-gain observer to achieve semi-global uniform ultimate boundedness. The efficacy of the proposed method is demonstrated through simulations. The simulations also show that by using adaptive control in conjunction with robust control, it is possible to tolerate larger approximation errors resulting from the use of lower-order networks.