Related papers: Autonomous Payload Thermal Control

Autonomous Payload Thermal Control

URL: http://arxiv.org/abs/2307.15438v3
Date: Mon, 2 Sep 2024 10:23:41 GMT
Title: Autonomous Payload Thermal Control
Authors: Alejandro D. Mousist,
Abstract summary: In small satellites there is less room for heat control equipment, scientific instruments, and electronic components. An autonomous thermal control tool that uses deep reinforcement learning is proposed for learning the thermal control policy onboard. The proposed framework is able to learn to control the payload processing power to maintain the temperature under operational ranges.
Score: 55.2480439325792
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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