Optimized Proportional-derivative Feedback-assisted Iterative Learning Control for Manipulator Trajectory Tracking

Abstract

Iterative learning control (ILC) is a popular scheme in the trajectory tracking of manipulators, greatly improving tracking accuracy despite often requiring multiple iterations over identical trajectories. This research introduces an optimization technique for ILC parameters, enhanced with proportional-derivative (PD) feedback control, which aims to significantly reduce tracking errors within a single iteration. In the proposed approach, a PD feedback controller is utilized in the first run, collecting error data. An ILC controller is then incorporated in the second run to minimize the tracking error. Utilizing the dynamic model of the system, the transcription method transforms the continuous-form optimization problem concerning the ILC parameters into a discrete form, enabling its solution via standard numerical optimization algorithms. To demonstrate the effectiveness of the proposed approach in reducing tracking errors, we compared the tracking errors for the first and second runs of the system using frequency-domain analysis and conducted simulations and experiments on two different trajectory types.