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.

翻译：在小卫星中，热控设备、科学仪器和电子元件的空间十分有限。此外，电子元件的紧密排列导致散热困难，存在无法恰当控制温度的风险，从而缩短组件寿命并影响任务性能。为解决这一挑战，利用星载智能水平日益提升的趋势，本文提出了一种基于深度强化学习的框架，该框架采用Soft Actor-Critic算法学习星上热控策略。该框架在简易模拟环境以及即将用于未来IMAGIN-e任务并部署于国际空间站（ISS）的真实空间边缘计算计算机上进行了评估。实验结果表明，所提出的框架能够学习控制载荷处理功率，将温度维持在运行范围内，从而补充传统热控系统。