Data-enabled Predictive Repetitive Control
<|reference_start|>Data-enabled Predictive Repetitive Control: 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.<|reference_end|>
@article{dinkla2024data-enabled,
    title={Data-enabled Predictive Repetitive Control},
    author={Rogier Dinkla, Tom Oomen, Sebastiaan Mulders, Jan-Willem van Wingerden},
    journal={arXiv preprint arXiv:2408.15210},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15210},
    primaryClass={eess.SY cs.SY}
}