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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/2307.15438 |
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| _version_ | 1866912460626198528 |
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| author | Mousist, Alejandro D. |
| author_facet | Mousist, Alejandro D. |
| contents | In small satellites there is less room for heat control equipment, scientific instruments, and electronic components. Furthermore, the near proximity of electronic components makes power dissipation difficult, with the risk of not being able to control the temperature appropriately, reducing component lifetime and mission performance. To address this challenge, taking advantage of the advent of increasing intelligence on board satellites, an autonomous thermal control tool that uses deep reinforcement learning is proposed for learning the thermal control policy onboard. The tool was evaluated in a real space edge processing computer that will be used in a demonstration payload hosted in the International Space Station (ISS). The experiment results show that the proposed framework is able to learn to control the payload processing power to maintain the temperature under operational ranges, complementing traditional thermal control systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_15438 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Autonomous Payload Thermal Control Mousist, Alejandro D. Machine Learning Systems and Control In small satellites there is less room for heat control equipment, scientific instruments, and electronic components. Furthermore, the near proximity of electronic components makes power dissipation difficult, with the risk of not being able to control the temperature appropriately, reducing component lifetime and mission performance. To address this challenge, taking advantage of the advent of increasing intelligence on board satellites, an autonomous thermal control tool that uses deep reinforcement learning is proposed for learning the thermal control policy onboard. The tool was evaluated in a real space edge processing computer that will be used in a demonstration payload hosted in the International Space Station (ISS). The experiment results show that the proposed framework is able to learn to control the payload processing power to maintain the temperature under operational ranges, complementing traditional thermal control systems. |
| title | Autonomous Payload Thermal Control |
| topic | Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2307.15438 |