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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Bibliographic Details
Published inInternational Journal of Aerospace Engineering Vol. 2022; pp. 1 - 18
Main Authors Li, Shiyao, Yan, Yushen, Qiao, Hao, Guan, Xin, Li, Xinguo
Format Journal Article
LanguageEnglish
Published New York Hindawi 07.06.2022
John Wiley & Sons, Inc
Hindawi Limited
Wiley
Subjects
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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Summary: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.
ISSN:1687-5966
1687-5974
DOI:10.1155/2022/2935929