An observer-based neural networks control scheme for nonlinear systems

The observer-based tracking control problem for a class of nonlinear affine systems using neural networks is proposed in this paper. The controller is based on feedback linearization where the observer system and control signal are directly estimated by a nonlinear in parameter neural networks (NLPNN). A Hebbian-like algorithm with e-modification is used to update the weights of the network. The uniformly ultimately boundedness of the tracking error and all signals in the overall closed-loop system is proved using Lyapunov´s direct method. To evaluate the performance of the proposed observer-based controller, a set of simulations is performed on a nonlinear cart-pole system. Simulation results show the effectiveness of the proposed control methodology.