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arXiv · 2408.15210

Data-enabled Predictive Repetitive Control

Rogier Dinkla·Tom Oomen·Sebastiaan Mulders·Jan-Willem van Wingerden
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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.

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Rogier Dinkla, Tom Oomen, Sebastiaan Mulders, Jan-Willem van Wingerden. 2024-08-27. Data-enabled Predictive Repetitive Control. https://arxiv.org/abs/2408.15210

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