Output feedback control of nonlinear systems using RBF neural networks

An adaptive output feedback control scheme for the output tracking of a cla ss of continuous-time nonlinear plants is presented. 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 paramete r projection, control saturation, and a high-gain observer to achieve semi- global uniform ultimate boundedness, The effectiveness of the proposed meth od is demonstrated through simulations. The simulations also show that by u sing adaptive control in conjunction with robust control, it is possible to tolerate larger approximation errors resulting from the use of lower order networks.