Trajectory Control of Manipulators Using an Adaptive Parametric Type-2 Fuzzy CMAC Friction and Disturbance Compensator

Friction and disturbances have an important influence on the robot manipulator dynamics. They are highly nonlinear terms that cannot be easily modeled. In this investigation an incrementally tuned parametric type-2 fuzzy cerebellar model articulation controller (P-T2FCMAC) neural network is proposed for compensation of friction and disturbance effects during the trajectory tracking control of rigid robot manipulators. CMAC networks have been widely applied to problems involving modeling and control of complex dynamical systems because of their computational simplicity, fast learning and good generalization capability. The integration of fuzzy logic systems and CMAC networks into fuzzy CMAC structures helps to improve their function approximation accuracy in terms of the CMAC weighting coefficients. Type-2 fuzzy logic systems are an area of growing interest over the last years since they are able to model uncertainties and to perform under noisy conditions in a better way than type-1 fuzzy systems. The proposed intelligent compensator makes use of a newly developed stable variable structure systems theory-based learning algorithm that can tune on-line the parameters of the membership functions and the weights in the fourth and fifth layer of the P-T2FCMAC network. Simulation results from the trajectory tracking control of two degrees of freedom RR planar robot manipulator using feedback linearization techniques and the proposed adaptive P-T2FCMAC neural compensator have shown that the joint positions are well controlled under wide variation of operation conditions and existing uncertainties.