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...

Full description

Saved in:
Bibliographic Details
Published in:Neural computing & applications Vol. 35; no. 9; pp. 6383 - 6404
Main Authors: Wang, Jinbo, Ma, Hongjun, Li, Huixu, Chen, Hongbo
Format: Journal Article
Language:English
Published: London Springer London 01.03.2023
Springer Nature B.V
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 classic indirect and direct optimal control methods. Considering the complex uncertainties of the preceding entry flight and potential unsettling hand-over conditions, a classification network is designed to classify the initial states of landing flights into different categories that correspond to bang–bang or non-bang–bang/singular thrust profiles. Thus, the subsequent online regression network can perform well for a large initial state distribution, and the algorithm adjusts to extensive landing situations. The combined application of classification and regression networks is one of the main contributions of the paper. The offline trained state-action regression networks generate guidance commands according to the real-time rocket state, obtaining a near-optimal landing trajectory. In addition, an online parallel trajectory simulation strategy is proposed to verify the trajectory quality, and an alternative trajectory optimization procedure is embedded into the proposed network-based framework to enhance the safety and accuracy of the guidance algorithm, representing another major contribution. Numerical experiments are presented to evaluate the effectiveness and accuracy of the proposed algorithm.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-08024-4