Fuzzy cerebellar model articulation controller and its application on robotic tracking control

Abstract

Cerebellar model articulation controller (CMAC) has simple structure and rapid learning speed,but its space division way can not be adapted on line.This hinders the improvement of its self-adaptive ability.In this paper,fuzzy theory is introduced to CMAC,and a fuzzy cerebellar model articulation controller(FCMAC) is proposed.By fuzzifying the space division way of CMAC and adapting it on line through BP learning algorithm,the proposed FCMAC can reflect the fuzziness and continuity of human cerebella,and greatly improve the self-adaptive ability of CMAC.The proposed FCMAC is applied on robotic tracking control system to counteract the disadvantageous influences of nonlinearities and uncertainties in robotic system.Simulation results show that the performance of proposed FCMAC is much better than that of traditional CMAC.