ITERATIVE LEARNING CONTROL FOR IMPROVED END-EFFECTOR ACCURACY OF AN INDUSTRIAL ROBOT

In this paper Iterative Learning Control is used to improve the tracking accuracy of the end-effector of an industrial robot. The learning control algorithm is based on a straightforward robot model and an optimisation criterium. The algorithm is tested on an industrial robot, where the end-effector motion is measured relative to a weld seam using a seam tracking sensor based on optical triangulation. The experiments show that the tracking error can be reduced considerably in a few iterations.