Robust adaptive sliding-mode control of condenser-cleaning mobile manipulator using fuzzy wavelet neural network

This paper presents a robust adaptive sliding-mode control (RASMC) scheme for a class of condenser-cleaning mobile manipulator (CCMM) in the presence of parametric uncertainties and external disturbances. The development of control system is based on the fuzzy wavelet neural network (FWNN). First, a dynamic model is obtained in view of the practical CCMM system. Second, the FWNN is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using learning ability of neural networks, RASMC can coordinately control the condenser-cleaning mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the adaptive sliding-mode control. Finally, based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimately boundedness of the tracking error are all strictly guaranteed. Moreover, simulation results validate the superior control performance of the proposed adaptive control method.