Real-time guidance for powered landing of reusable rockets via deep learning

This paper focuses on improving the autonomy and efficiency of fuel-optimal powered landing guidance for reusable rockets considering aerodynamic forces. Deep-learning-based methods are developed to enable online and autonomous operation ability and to avoid the convergence problem encountered by cl...

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Vydáno v:	Neural computing & applications Ročník 35; číslo 9; s. 6383 - 6404
Hlavní autoři:	Wang, Jinbo, Ma, Hongjun, Li, Huixu, Chen, Hongbo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.03.2023 Springer Nature B.V
Témata:	Aerodynamic forces Algorithms Artificial Intelligence Autonomy Classification Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Control methods Data Mining and Knowledge Discovery Deep learning Image Processing and Computer Vision Landing Machine learning Optimal control Original Article Probability and Statistics in Computer Science Real time Regression Reusable rocket engines Rockets Spacecraft guidance Trajectory optimization Deep learning Intelligent trajectory optimization Classification network Reusable rocket landing
ISSN:	0941-0643, 1433-3058
On-line přístup:	Získat plný text
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