Data-enabled Predictive Repetitive Control

TL;DR

This paper introduces DeePRC, a novel data-driven repetitive control method that leverages system lifting and an extended Willems' lemma to effectively mitigate periodic disturbances in noisy environments.

Contribution

The paper develops a data-driven repetitive control framework that extends Willems' fundamental lemma to systems with disturbances, enabling effective disturbance attenuation.

Findings

DeePRC successfully mitigates periodic disturbances in simulations.

The method is robust to noise in the system.

It extends traditional model-based approaches with a data-driven technique.

Abstract

Many systems are subject to periodic disturbances and exhibit repetitive behaviour. Model-based repetitive control employs knowledge of such periodicity to attenuate periodic disturbances and has seen a wide range of successful industrial implementations. The aim of this paper is to develop a data-driven repetitive control method. In the developed framework, linear periodically time-varying (LPTV) behaviour is lifted to linear time-invariant (LTI) behaviour. Periodic disturbance mitigation is enabled by developing an extension of Willems' fundamental lemma for systems with exogenous disturbances. The resulting Data-enabled Predictive Repetitive Control (DeePRC) technique accounts for periodic system behaviour to perform attenuation of a periodic disturbance. Simulations demonstrate the ability of DeePRC to effectively mitigate periodic disturbances in the presence of noise.