Receding Horizon Iterative Learning Control for Continuously Operated Systems

TL;DR

This paper introduces a receding horizon iterative learning control scheme for continuously operated systems without initial condition resets, utilizing a lifted system representation and multi-iteration cost optimization.

Contribution

It develops a novel lifted system model and an economic cost function for ILC, enabling prediction horizons beyond a single iteration for continuous operation.

Findings

Proven convergence for varying initial conditions and inputs.

Demonstrated effectiveness on a simulated servo-positioning system.

Enhanced prediction horizon improves control performance.

Abstract

This paper presents an iterative learning control (ILC) scheme for continuously operated repetitive systems for which no initial condition reset exists. To accomplish this, we develop a lifted system representation that accounts for the effect of the initial conditions on dynamics and projects the dynamics over multiple future iterations. Additionally, we develop an economic cost function and update law that considers the performance over multiple iterations in the future, thus allowing for the prediction horizon to be larger than just the next iteration. Convergence of the iteration varying initial condition and applied input are proven and demonstrated using a simulated servo-positioning system test case.