Propulsionless planar phasing of multiple satellites using deep reinforcement learning
| Autor: | Melrose Brown, Joshua Abbey, Sudantha Balage, Rasit Abay, Russell Boyce, Brenton Smith |
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| Rok vydání: | 2021 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Spacecraft business.industry Computer science Work (physics) Aerospace Engineering Astronomy and Astrophysics Control engineering 01 natural sciences Phaser Geophysics Planar Space and Planetary Science Physics::Space Physics 0103 physical sciences Key (cryptography) General Earth and Planetary Sciences Reinforcement learning Satellite business 010303 astronomy & astrophysics 0105 earth and related environmental sciences |
| Zdroj: | Advances in Space Research. 67:3667-3682 |
| ISSN: | 0273-1177 |
| DOI: | 10.1016/j.asr.2020.09.025 |
| Popis: | This work creates a framework for solving highly non-linear satellite formation control problems by using model-free policy optimisation deep reinforcement learning (DRL) methods. This work considers, believed to be for the first time, DRL methods, such as advantage actor-critic method (A2C) and proximal policy optimisation (PPO), to solve the example satellite formation problem of propellantless planar phasing of multiple satellites. Three degree-of-freedom simulations, including a novel surrogate propagation model, are used to train the deep reinforcement learning agents. During training, the agents actuated their motion through cross-sectional area changes which altered the environmental accelerations acting on them. The DRL framework designed in this work successfully coordinated three spacecraft to achieve a propellantless planar phasing manoeuvre. This work has created a DRL framework that can be used to solve complex satellite formation flying problems, such as planar phasing of multiple satellites and in doing so provides key insights into achieving optimal and robust formation control using reinforcement learning. |
| Databáze: | OpenAIRE |
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