Discrete-time neural control for electrically driven nonholonomic mobile robots

An inverse optimal neural controller for discrete-time unknown nonlinear systems, in the presence of external disturbances and parameter uncertainties, is presented. It is based on a discrete-time recurrent high-order neural network trained with an extended Kalman filter-based algorithm. The applicability of the proposed approach is first tested via simulations for an electrically driven nonholonomic mobile robot, and finally, the proposed methodology is implemented on real time. Copyright � 2012 John Wiley & Sons, Ltd