Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control
<|reference_start|>Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control: Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive Control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.<|reference_end|>
@article{dinkla2024closed-loop,
    title={Closed-loop Data-Enabled Predictive Control and its equivalence with
  Closed-loop Subspace Predictive Control},
    author={Rogier Dinkla, Sebastiaan Mulders, Tom Oomen, Jan-Willem van Wingerden},
    journal={arXiv preprint arXiv:2402.14374},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14374},
    primaryClass={eess.SY cs.SY}
}