Cross-Coupled Iterative Learning Control for Robot Trajectories

Abstract:
A typical movement of industrial robots requires the coordinated motion of multiple axes, with strongly coupled dynamics. Both, axial and contour errors are compensated to accurately follow a trajectory. Axial errors describe the control errors of the axes at each moment and contour errors the distance of the end-effector to the spatial reference trajectory. An exact following of the trajectory has to be traded off against matching the current target position, depending on the application. This paper presents a model-free, cross-coupled, iterative learning control architecture for three axes to compensate a robot's movement. A comparison between the iterative learning control and the cross-coupled iterative learning control is presented. It is shown that the design results improving the axial and contour accuracy of the robot.