Research on Articulated Robot Control Based on High-order Internal Model Iterative Learning

In order to improve the tracking accuracy and response speed of the articulated robot in the working process under non-strict repetitive conditions, a three-joint articulated robot model is designed, and the kinematics and dynamics analysis are carried out to verify the reasonable structure of the model. In view of the non-repetitive and nonlinear characteristics of the articulated robot system, it is proposed that a high-order internal model iterative learning control algorithm can be applied to the control of the articulated robot system. A reasonable learning gain and a higher internal model order are designed to strictly prove its convergence in theory. The simulation contrast experiment and the trajectory tracking experiment after adding the disturbance are designed. The results show that the high-order internal model iterative learning algorithm converges faster and has good control effect.