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Autonomous orbital maintenance using a supervised-learning-based target point approach
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DOI：10.1016/j.actaastro.2026.02.008.png)
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摘要
En 中文
• Supervised Learning enables autonomous stationkeeping using Target Point Approach. • Lightweight neural networks assess feasibility and predict stationkeeping parameters. • Data processing based on distributions improves stationkeeping parameter prediction. • Large-scale simulations show robust long-term autonomous stationkeeping performance.
Keyword:
Spacecraft autonomy
Autonomous orbital maintenance
Supervised learning
Target point approach
Stochastic optimization
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期刊
IF:
3.4
论文数:
1.2W
被引数:
2.1W
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