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Adaptive satellite attitude control for varying masses using deep reinforcement learning

Authors :
Wiebke Retagne
Jonas Dauer
Günther Waxenegger-Wilfing
Source :
Frontiers in Robotics and AI, Vol 11 (2024)
Publication Year :
2024
Publisher :
Frontiers Media S.A., 2024.

Abstract

Traditional spacecraft attitude control often relies heavily on the dimension and mass information of the spacecraft. In active debris removal scenarios, these characteristics cannot be known beforehand because the debris can take any shape or mass. Additionally, it is not possible to measure the mass of the combined system of satellite and debris object in orbit. Therefore, it is crucial to develop an adaptive satellite attitude control that can extract mass information about the satellite system from other measurements. The authors propose using deep reinforcement learning (DRL) algorithms, employing stacked observations to handle widely varying masses. The satellite is simulated in Basilisk software, and the control performance is assessed using Monte Carlo simulations. The results demonstrate the benefits of DRL with stacked observations compared to a classical proportional–integral–derivative (PID) controller for the spacecraft attitude control. The algorithm is able to adapt, especially in scenarios with changing physical properties.

Details

Language :
English
ISSN :
22969144
Volume :
11
Database :
Directory of Open Access Journals
Journal :
Frontiers in Robotics and AI
Publication Type :
Academic Journal
Accession number :
edsdoj.40ae4607959a43abba6c73ac2283dcfb
Document Type :
article
Full Text :
https://doi.org/10.3389/frobt.2024.1402846