A Nested-Loop Iterative Learning Control for Robot Manipulators

Abstract

To improve the tracking performance of industrial robot manipulators, a nested-loop iterative learning control (ILC) structure is presented. It consists of an inner loop that deals with drive dynamics, and an outer loop which addresses impreciseness of kinematic parameters as well as joint static bias. A data-based frequency inversion technique with motion constraints is utilized for fast inner loop convergence. The outer loop measures the end effector deviation with a laser tracker and uses inverse Jacobian matrix for joint reference modification. Analysis of the algorithm is given, and is experimentally demonstrated on a six degree-of-freedom robot manipulator. It is shown that the proposed method mitigates the maximum dynamic tracking error by an order of magnitude, and is applicable to different payloads due to small system variation from torque shielding of gear reduction.