Multi-Objective Learning Model Predictive Control
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909353768910848 |
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| author | Nair, Siddharth H. Vallon, Charlott Borrelli, Francesco |
| author_facet | Nair, Siddharth H. Vallon, Charlott Borrelli, Francesco |
| contents | Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a linear system's closed-loop performance with respect to several convex control objectives over iterations of a repeated task. At each task iteration, collected system data is used to construct terminal components of a Model Predictive Controller. The formulation presented in this paper ensures that closed-loop control performance improves between successive iterations with respect to each objective. We provide proofs of recursive feasibility and performance improvement, and show that the converged policy is Pareto optimal. Simulation results demonstrate the applicability of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11698 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Objective Learning Model Predictive Control Nair, Siddharth H. Vallon, Charlott Borrelli, Francesco Systems and Control Multi-Objective Learning Model Predictive Control is a novel data-driven control scheme which improves a linear system's closed-loop performance with respect to several convex control objectives over iterations of a repeated task. At each task iteration, collected system data is used to construct terminal components of a Model Predictive Controller. The formulation presented in this paper ensures that closed-loop control performance improves between successive iterations with respect to each objective. We provide proofs of recursive feasibility and performance improvement, and show that the converged policy is Pareto optimal. Simulation results demonstrate the applicability of the proposed approach. |
| title | Multi-Objective Learning Model Predictive Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2405.11698 |