Trajectory control of manipulators using type-2 fuzzy neural friction and disturbance compensator

Abstract

An incrementally tuned interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network implementing fuzzy if-then rule base with first order output functions is proposed for compensation of friction and disturbance effects during the trajectory tracking control of rigid robot manipulators. Friction and disturbances have an important influence on the robot manipulator dynamics. They are highly nonlinear terms that cannot be easily modeled. The proposed intelligent compensator makes use of a newly developed stable Variable Structure Systems theory-based on-line learning algorithm that is also able to adapt the existing relation between the lower and the upper membership functions of the type-2 fuzzy system. This allows managing of non-uniform uncertainties. Simulation results from the trajectory tracking control of two degrees of freedom RR planar robot manipulator using feedback linearization techniques and the proposed adaptive interval type-2 fuzzy neural compensator have shown that the joint positions are well controlled under wide variation of operation conditions and existing uncertainties.