Research on Fuzzy Adaptive and PD-Type Iterative Learning Control for Robot Manipulator

Abstract

In industrial production, the robot arm often carries out repetitive operations such as moving objects, which leads to the problem of motion accuracy decline. Combining the advantages of fuzzy control and iterative learning control, this paper presents a fuzzy self-adaptive PD-type iterative learning control method. Taking the double joint manipulator as the research object and the Fuzzy control rules are written by using the Fuzzy toolbox. The fuzzy controller is used to modify PD parameters in real-time to improve the adaptability of the system. The trajectory tracking control model of the manipulator is built in Simulink. The two control strategies of PD iterative learning control and the proposed method are compared. The simulation results show that the error generated by the proposed control method is less than the former one, and the error convergence speed is faster, and the overall control effect is quite well.