Carradori, Jacopo und Sagliano, Marco und Mooij, Erwin (2025) Transformer-Based Robust Feedback Guidance for Atmospheric Powered Landing. In: AIAA SciTech 2025 Forum, Seiten 1-23. American Institute of Aeronautics and Astronautics. AIAA Scitech 2025, 2025-01-06 - 2025-01-10, Orlando, United States. doi: 10.2514/6.2025-2771. ISBN 978-162410723-8.
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Offizielle URL: https://doi.org/10.2514/6.2025-2771
Kurzfassung
Rocket reusability is a key factor in enabling quicker and more cost-effective access to space. However, landing on Earth poses significant challenges due to the dynamic and highly uncertain environment. A robust Guidance, Navigation, and Control system is essential to guide the vehicle to the landing site while meeting terminal constraints and minimizing fuel consumption. This research integrates Meta-Reinforcement Learning with Gated Transformer XL Neural Networks to enhance the robustness of the powered guidance with respect to atmospheric and aerodynamic uncertainties, navigation and control errors, and dispersed initial conditions. By employing a 6-Degrees-of-Freedom dynamics model and accurate vehicle and environmental simulations, the agent learns a higher fidelity guidance policy compared to existing literature, demonstrating successful and robust performance in Monte Carlo simulations. In this complex scenario, the innovative attention-based neural networks also outperform recurrent neural networks, widely used for Reinforcement Learning-based space guidance applications.
elib-URL des Eintrags: | https://elib.dlr.de/216757/ | ||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vorlesung) | ||||||||||||||||
Titel: | Transformer-Based Robust Feedback Guidance for Atmospheric Powered Landing | ||||||||||||||||
Autoren: |
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Datum: | Januar 2025 | ||||||||||||||||
Erschienen in: | AIAA SciTech 2025 Forum | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||
DOI: | 10.2514/6.2025-2771 | ||||||||||||||||
Seitenbereich: | Seiten 1-23 | ||||||||||||||||
Verlag: | American Institute of Aeronautics and Astronautics | ||||||||||||||||
ISBN: | 978-162410723-8 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | 6-DoF Rocket Landing; AI; Machine Learning; Transformers; Meta-Reinforcement Learning; Optimal Control; Neural Networks | ||||||||||||||||
Veranstaltungstitel: | AIAA Scitech 2025 | ||||||||||||||||
Veranstaltungsort: | Orlando, United States | ||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||
Veranstaltungsbeginn: | 6 Januar 2025 | ||||||||||||||||
Veranstaltungsende: | 10 Januar 2025 | ||||||||||||||||
Veranstalter : | American Institute of Aeronautics and Astronautics | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||
HGF - Programmthema: | Raumtransport | ||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
DLR - Forschungsgebiet: | R RP - Raumtransport | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Projekt CALLISTO [RP] | ||||||||||||||||
Standort: | Bremen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Raumfahrtsysteme > Navigations- und Regelungssysteme | ||||||||||||||||
Hinterlegt von: | Sagliano, Marco | ||||||||||||||||
Hinterlegt am: | 26 Sep 2025 10:05 | ||||||||||||||||
Letzte Änderung: | 26 Sep 2025 10:05 |
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