Data-Enabled Predictive Control for Flexible Spacecraft
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915150602174464 |
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| author | Wang, Huanqing Zhang, Kaixiang Vahidi-Moghaddam, Amin An, Haowei Li, Nan Huang, Daning Li, Zhaojian |
| author_facet | Wang, Huanqing Zhang, Kaixiang Vahidi-Moghaddam, Amin An, Haowei Li, Nan Huang, Daning Li, Zhaojian |
| contents | Spacecraft are vital to space exploration and are often equipped with lightweight, flexible appendages to meet strict weight constraints. These appendages pose significant challenges for modeling and control due to their inherent nonlinearity. Data-driven control methods have gained traction to address such challenges. This paper introduces, to the best of the authors' knowledge, the first application of the data-enabled predictive control (DeePC) framework to boundary control for flexible spacecraft. Leveraging the fundamental lemma, DeePC constructs a non-parametric model by utilizing recorded past trajectories, eliminating the need for explicit model development. The developed method also incorporates dimension reduction techniques to enhance computational efficiency. Through comprehensive numerical simulations, this study compares the proposed method with Lyapunov-based control, demonstrating superior performance and offering a thorough evaluation of data-driven control for flexible spacecraft. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09531 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Enabled Predictive Control for Flexible Spacecraft Wang, Huanqing Zhang, Kaixiang Vahidi-Moghaddam, Amin An, Haowei Li, Nan Huang, Daning Li, Zhaojian Systems and Control Spacecraft are vital to space exploration and are often equipped with lightweight, flexible appendages to meet strict weight constraints. These appendages pose significant challenges for modeling and control due to their inherent nonlinearity. Data-driven control methods have gained traction to address such challenges. This paper introduces, to the best of the authors' knowledge, the first application of the data-enabled predictive control (DeePC) framework to boundary control for flexible spacecraft. Leveraging the fundamental lemma, DeePC constructs a non-parametric model by utilizing recorded past trajectories, eliminating the need for explicit model development. The developed method also incorporates dimension reduction techniques to enhance computational efficiency. Through comprehensive numerical simulations, this study compares the proposed method with Lyapunov-based control, demonstrating superior performance and offering a thorough evaluation of data-driven control for flexible spacecraft. |
| title | Data-Enabled Predictive Control for Flexible Spacecraft |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2502.09531 |