Calibration-Based Iterative Learning Control for Path Tracking of Industrial Robots

Abstract

This paper addresses the problem of path tracking of industrial robots. The main idea is to correct a preplanned path through an iterative learning control (ILC) method. Instead of seeking the conventional ILC strategy, an iterative learning identification method, which is called calibration-based ILC, is developed to identify the robot kinematic parameters along the path in a local working zone. To facilitate calibration-based ILC, we propose two objectives. The first objective is to find the exact values of robot kinematic parameters based on the ILC scheme. The second objective is to search the fastest learning convergence speed and robustness in the iterative domain. Based on the identification of robot kinematic parameters, we then propose an algorithm for the accurate path tracking of industrial robots. The simulation and experimental results demonstrate that the performance of path tracking can be improved significantly via the proposed method.