Trajectory tracking control of SCARA parallel robot based on fuzzy adaptive iterative learning algorithm

Abstract

Aiming to improve the anti-interference capability and trajectory tracking accuracy of the robot control system that undertakes repetitive motion tasks, this work presents the trajectory tracking control along with a four degree-of-freedom parallel robot. By means of the principle of virtual work, the dynamic model of the robot was built for the model-based control design. A fuzzy adaptive iterative learning control algorithm was proposed by combining fuzzy control theory and iterative learning theory. Finally, the proposed algorithm was simulated and verified in comparison with the traditional iterative learning control algorithm, of which the results show that the proposed algorithm has robustness and higher tracking accuracy than the traditional one.