Performance Improvement of Industrial Robot Trajectory Tracking Using Adaptive-Learning Scheme

Abstract

More and more industrial robot operations demand high-accuracy trajectory performance which may not be achievable by using conventional PID control. This paper describes a new adaptive control method with a learning ability in the repetitive tasks, called the Adaptive-Learning (A-L) scheme. The method is based on the proposed theory of two operational modes: the single operational mode and the repetitive operational mode. In the single operational mode, the control is an adaptive control with a new parameter adaptation law using information from the previous trials. In the repetitive operational mode, the control is a model-based iterative learning control. The advantage of the A-L scheme lies in the ability to guarantee convergence in both modes. Theoretical analysis and experimental evaluation on a commercial robot demonstrate the effectiveness of the A-L scheme in controlling an industrial robot manipulator.