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| Main Author: | |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.14409 |
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| _version_ | 1866913280645136384 |
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| author | Malikopoulos, Andreas A. |
| author_facet | Malikopoulos, Andreas A. |
| contents | In this paper, we provide a theoretical framework that separates the control and learning tasks in a linear system. This separation allows us to combine offline model-based control with online learning approaches and thus circumvent current challenges in deriving optimal control strategies in applications where a large volume of data is added to the system gradually in real time and not altogether in advance. We provide an analytical example to illustrate the framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_14409 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Combining Learning and Control in Linear Systems Malikopoulos, Andreas A. Optimization and Control In this paper, we provide a theoretical framework that separates the control and learning tasks in a linear system. This separation allows us to combine offline model-based control with online learning approaches and thus circumvent current challenges in deriving optimal control strategies in applications where a large volume of data is added to the system gradually in real time and not altogether in advance. We provide an analytical example to illustrate the framework. |
| title | Combining Learning and Control in Linear Systems |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2310.14409 |