Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916134086770688 |
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| author | Dinkla, Rogier Mulders, Sebastiaan Oomen, Tom van Wingerden, Jan-Willem |
| author_facet | Dinkla, Rogier Mulders, Sebastiaan Oomen, Tom van Wingerden, Jan-Willem |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14374 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control Dinkla, Rogier Mulders, Sebastiaan Oomen, Tom van Wingerden, Jan-Willem Systems and 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. |
| title | Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2402.14374 |