Dual Design Iterative Learning Controller for Robotic Manipulator Application

Abstract

Iterative learning control enables high precision performance through observed historical data in previous iterations. Several techniques for designing iterative learning controllers have been developed in the existing literature. However, evidence to support the design’s efficiency in real applications is, unfortunately, missing in some designs. This paper presents a practical iterative learning controller design, so-called the dual design, combining two existing controller designs using the weighted sum technique. The two controllers are designed using data-driven and frequency response approaches, distinctively selected to take benefits from each. A single gain controller designed from the gain adjustment mechanism usually has slow learning behavior but can be very robust to external uncertainty. The other design imitating the inverse of the frequency response of the system can learn extremely fast. However, its performance may not be as effective as desired when the frequency response of the system is incorrectly perceived. By taking advantage of both controllers, the dual design can achieve fast-learning behavior as well as robustness to external disturbances. Simulation and experiments were carried out to demonstrate the design efficiency.