Resilient Operation Planning for CubeSat Using Reinforcement Learning



Published Sep 4, 2023
Shuntaro Kuroiwa Nozomu Kogiso


This study proposes an autonomous operation procedure for a CubeSat by applying reinforcement learning based on resilient engineering. The CubeSat requires rapid judgment in every visible window based on a sufficient understanding of the health conditions of the satellite from limited telemetry data due to the limited communication performance and poor protection functions from the harsh environment. This study first performs a risk analysis by using System Theoretic Process Analysis (STPA) to evaluate the risk scenario of the Cube-Sat. In order to successfully operate the missions while avoiding the risk scenarios, reinforcement learning is applied to learn adequate behaviors according to the satellite situations such as the temperature and voltage of the installed battery, the sunlight and eclipse phase and the mission progress and plan. Through numerical examples, the validity of the proposed method is illustrated.

Abstract 124 | PDF Downloads 127



CubeSat, Reinforcement Learning, Operation Planning, STPA, Risk Analysis

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