Autonomous Payload Thermal Control
- URL: http://arxiv.org/abs/2307.15438v2
- Date: Tue, 22 Aug 2023 09:05:08 GMT
- Title: Autonomous Payload Thermal Control
- Authors: Alejandro D. Mousist
- Abstract summary: The proposed framework is able to learn to control the payload processing power to maintain the temperature under operational ranges.
The framework will be shipped in the future IMAGIN-e mission and hosted in the ISS.
- Score: 65.268245109828
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In small satellites there is less room for heat control equipment, scientific
instruments, and electronic components. Furthermore, the near proximity of the
electronics 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, a deep reinforcement learning
based framework that uses Soft Actor-Critic algorithm is proposed for learning
the thermal control policy onboard. The framework is evaluated both in a naive
simulated environment and in a real space edge processing computer that will be
shipped in the future IMAGIN-e mission and hosted in the 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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