Reinforcement Learning for Computational Guidance of Launch Vehicle Upper Stage
This manuscript investigates the use of a reinforcement learning method for the guidance of launch vehicles and a computational guidance algorithm based on a deep neural network (DNN). Computational guidance algorithms can deal with emergencies during flight and improve the success rate of missions,...
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| Published in | International Journal of Aerospace Engineering Vol. 2022; pp. 1 - 18 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Hindawi
07.06.2022
John Wiley & Sons, Inc Hindawi Limited Wiley |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This manuscript investigates the use of a reinforcement learning method for the guidance of launch vehicles and a computational guidance algorithm based on a deep neural network (DNN). Computational guidance algorithms can deal with emergencies during flight and improve the success rate of missions, and most of the current computational guidance algorithms are based on optimal control, whose calculation efficiency cannot be guaranteed. However, guidance-based DNN has high computational efficiency. A reward function that satisfies the flight process and terminal constraints is designed, then the mapping from state to control is trained by the state-of-the-art proximal policy optimization algorithm. The results of the proposed algorithm are compared with results obtained by the guidance-based optimal control, showing the effectiveness of the proposed algorithm. In addition, an engine failure numerical experiment is designed in this manuscript, demonstrating that the proposed algorithm can guide the launch vehicle to a feasible rescue orbit. |
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| AbstractList | This manuscript investigates the use of a reinforcement learning method for the guidance of launch vehicles and a computational guidance algorithm based on a deep neural network (DNN). Computational guidance algorithms can deal with emergencies during flight and improve the success rate of missions, and most of the current computational guidance algorithms are based on optimal control, whose calculation efficiency cannot be guaranteed. However, guidance-based DNN has high computational efficiency. A reward function that satisfies the flight process and terminal constraints is designed, then the mapping from state to control is trained by the state-of-the-art proximal policy optimization algorithm. The results of the proposed algorithm are compared with results obtained by the guidance-based optimal control, showing the effectiveness of the proposed algorithm. In addition, an engine failure numerical experiment is designed in this manuscript, demonstrating that the proposed algorithm can guide the launch vehicle to a feasible rescue orbit. |
| Audience | Academic |
| Author | Qiao, Hao Yan, Yushen Li, Xinguo Li, Shiyao Guan, Xin |
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| Copyright | Copyright © 2022 Shiyao Li et al. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Shiyao Li et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Aerospace engineering Algorithms Artificial neural networks Deep learning Engine failure Machine learning Markov analysis Mathematical optimization Neural networks Optimal control Optimization R&D Research & development Terminal constraints |
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| Title | Reinforcement Learning for Computational Guidance of Launch Vehicle Upper Stage |
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