Cross-Coupled Iterative Learning Control for Complex Systems: A Monotonically Convergent and Computationally Efficient Approach

TL;DR

This paper introduces a norm-optimal, cross-coupled iterative learning control framework that ensures monotonic convergence and computational efficiency for complex manufacturing systems, using offline contour errors and iteration-varying weights.

Contribution

It develops a novel monotonic convergence condition for iteration-varying ILC with contour errors and proposes a resource-efficient implementation via linear quadratic tracking reformulation.

Findings

Monotonic convergence conditions are established for the proposed ILC.

The resource-efficient implementation significantly reduces computational load.

Simulation results demonstrate the effectiveness of the approach.

Abstract

Cross-coupled iterative learning control (ILC) can achieve high performance for manufacturing applications in which tracking a contour is essential for the quality of a product. The aim of this paper is to develop a framework for norm-optimal cross-coupled ILC that enables the use of exact contour errors that are calculated offline, and iteration- and time-varying weights. Conditions for the monotonic convergence of this iteration-varying ILC algorithm are developed. In addition, a resource-efficient implementation is proposed in which the ILC update law is reframed as a linear quadratic tracking problem, reducing the computational load significantly. The approach is illustrated on a simulation example.