End-to-End ILC for Repetitive Untrackable Tasks: A Cooperative Game Perspective

TL;DR

This paper proposes an end-to-end iterative learning control method for repetitive tasks that are untrackable, using a cooperative game perspective to improve performance in closed-loop systems.

Contribution

It introduces a novel two-player ILC design that updates reference and control inputs simultaneously, addressing untrackable tasks in a closed-loop setting.

Findings

A sufficient condition for improved performance over NOILC is established.

The method effectively handles untrackable repetitive tasks.

Numerical example confirms the approach's effectiveness.

Abstract

An inherent assumption of perfect tracking in iterative learning control (ILC) is that there exists an ILC input such that the generated output can track the desired trajectory reference. This assumption may fail in practice, which gives rise to desired but untrackable tasks. This paper gives an end-to-end ILC design for repetitive untrackable tasks in closed-loop systems. The reference input is trial-to-trial updated together with the ILC feedforward input based on the measurement data. This two-player behavior of the closed-loop ILC system is investigated from a cooperative game perspective. A sufficient condition for the two-player end-to-end ILC to have a lower cost than the one-player norm optimal ILC (NOILC) is discovered. Finally, a numerical example is given to verify the effectiveness of the developed method.