CEAS EuroGNC 2026
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Machine Learning for Parameter Estimation in CubeSats: A preliminary study for ST3LLARsat1
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| Jorge J. Ilarraza-Zuazo |
Department of Aerospace Engineering, Universidad Carlos III de Madrid (UC3M), Leganés, Spain. | | Andrés Marcos |
Department of Aerospace Engineering, Universidad Carlos III de Madrid (UC3M), Leganés, Spain. |
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| Abstract:
Machine Learning methods are transforming many aspects of engineering by enabling the design and operation of systems that feature more advanced objectives thanks to the methods' predictive, autonomous, and data-driven capabilities. Cube/Nano-satellites are a type of aerospace system especially positioned to benefit from the use of these methods, given their very limited budget and onboard computational power. This article presents the application of the technique known as Compressed Sensing to the estimation of the inertial parameters of UC3M's ST3LLARsat1 "Boira" CubeSat during the detumbling phase. Its physical characteristics, detumbling controller, sensor/actuator models, and orbital parameters are implemented and simulated on an open-source CubeSat simulator, and a Monte Carlo campaign is performed randomly varying the initial conditions. The results are very promising, with a maximum estimation error of 1%.
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| Keywords: Machine Learning; Compresses Sensing; Attitude Estimation; CubeSats |
 View PDF CEAS-GNC-2026-045 doi: 10.82124/CEAS-GNC-2026-045 |
| Jorge J. Ilarraza-Zuazo, Andrés Marcos: Machine Learning for Parameter Estimation in CubeSats: A preliminary study for ST3LLARsat1. Proceedings of the 2026 CEAS EuroGNC conference. Madrid, Spain. May 2026. doi: 10.82124/CEAS-GNC-2026-045.
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| BibTeX entry (UTF-8):
@Incollection{CEAS-GNC-2026-045,
author = {Ilarraza-Zuazo, Jorge J. and Marcos, Andrés},
title = {Machine Learning for Parameter Estimation in CubeSats: A preliminary study for ST3LLARsat1},
booktitle = {Proceedings of the 2026 {CEAS EuroGNC} conference},
address = {Madrid, Spain},
month = may,
year = {2026},
doi = {10.82124/CEAS-GNC-2026-045}
}
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