A Novel Self-organizing Fuzzy Cerebellar Model Articulation Controller Based Overlapping Gaussian Membership Function for Controlling Robotic System

This paper introduces an effective intelligent controller for robotic systems with uncertainties. The proposed method is a novel self-organizing fuzzy cerebellar model articulation (NSOFC) which combination of (CMAC) and sliding mode control (SMC). We also present new Gaussian membership function (GMF) that designed by the prior current GMF each layer CMAC. In addition, relevant data used to check tracking errors more accurately. inputs can be mixed simultaneously between states according corresponding errors. Moreover, uses approach increase or decrease number layers, therefore structures NSOFC adjusted automatically. consists compensation controller. estimate ideal controller, eliminate approximated error. online parameters tuning law based on Lyapunov’s theory ensure stability system. Finally, experimental results 2 DOF robot arm are demonstrate efficiency