Autonomous Payload Thermal Control

TL;DR

This paper proposes an autonomous thermal control system for small satellites using deep reinforcement learning, capable of maintaining optimal temperatures and complementing traditional methods, demonstrated on a space edge computer in the ISS.

Contribution

Introduction of a deep reinforcement learning-based thermal control tool for small satellites, enabling onboard autonomous temperature regulation.

Findings

Successfully learned to control payload temperature within operational ranges

Demonstrated effectiveness on a real space edge processing computer in the ISS

Complemented traditional thermal control systems with autonomous decision-making

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.