DESIGN AN ADAPTIVE WAVELET NEURAL NETWORK CONTROLLER FOR A TWO-WHEELED MOBILE ROBOT LIKE VEHICLE

摘要

This paper presents an analytical approach to design an adaptive backstepping wavelet neural network (WNN)-based controller for global asymptotic stabilization of a two-wheeled mobile robot (TWMR). It is assumed that the dynamics model is unknown, and also system exposed to an external disturbance. The design method is based on the concept of backstepping method. At the first level, adaptive backstepping controller is employed. This controller is taking advantage of WNN identifier for estimating of unknown plant dynamics. Moreover, since the adaption laws of controller are extracted in sense of Lyapunov function, the stability of closed loop is guaranteed. At the second level, robust controller is combined with primary controller, which results L 2 tracking performance and comforts lumped uncertainties exit in control system due to approximation error and external disturbance. Finally, a numerical example for the proposed control scheme is presented.