Robust Data-Driven Receding Horizon Control
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912633603489792 |
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| author | Zheng, Jian Kiani, Sahand Sznaier, Mario Lagoa, Constantino |
| author_facet | Zheng, Jian Kiani, Sahand Sznaier, Mario Lagoa, Constantino |
| contents | This paper presents a data-driven receding horizon control framework for discrete-time linear systems that guarantees robust performance in the presence of bounded disturbances. Unlike the majority of existing data-driven predictive control methods, which rely on Willem's fundamental lemma, the proposed method enforces set-membership constraints for data-driven control and utilizes execution data to iteratively refine a set of compatible systems online. Numerical results demonstrate that the proposed receding horizon framework achieves better contractivity for the unknown system compared with regular data-driven control approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Robust Data-Driven Receding Horizon Control Zheng, Jian Kiani, Sahand Sznaier, Mario Lagoa, Constantino Optimization and Control This paper presents a data-driven receding horizon control framework for discrete-time linear systems that guarantees robust performance in the presence of bounded disturbances. Unlike the majority of existing data-driven predictive control methods, which rely on Willem's fundamental lemma, the proposed method enforces set-membership constraints for data-driven control and utilizes execution data to iteratively refine a set of compatible systems online. Numerical results demonstrate that the proposed receding horizon framework achieves better contractivity for the unknown system compared with regular data-driven control approaches. |
| title | Robust Data-Driven Receding Horizon Control |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2510.06153 |