Neural Network Reentry Guidance for Reusable Launch Vehicle Based on Sample Dimensionality Reduction
Publication: Journal of Aerospace Engineering
Volume 37, Issue 5
Abstract
In this paper, a neural network guidance based on sample dimension reduction is proposed for a reusable launch vehicle. Different from previous works, the training samples of neural networks use Pearson correlation matrix analysis for dimension reduction, which significantly reduces the data volume of training samples and improves the efficiencies of sample construction, data storage, and neural network training. First, the analytical samples are generated by integrating dimensionless energy longitudinal motion equations. Then, the input parameters are processed by Pearson correlation matrix analysis, significantly reducing the data volume of training samples while ensuring the training effectiveness. Furthermore, the neural network is utilized to learn the mapping relationship between reduced-dimension flight states and predictive range-to-go, eliminating numerical integration calculations and improving the real-time performance of guidance. Finally, an extended Kalman filter (EKF) is implemented online for aerodynamic parameter identification, which dramatically enhances the adaptability to internal uncertainties and external disturbances. Compared with the traditional numerical guidance method, simulation results demonstrate that the proposed guidance exhibits superior real-time performance and faster computation speed while ensuring high accuracy and strong robustness to perturbations.
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Data Availability Statement
All data, models, and code generated or used during the study appear in the published article.
Acknowledgments
This work was partly supported by the National Natural Science Foundation of China, Grant 11202024.
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Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 37 • Issue 5 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jun 5, 2023
Accepted: Mar 18, 2024
Published online: Jul 9, 2024
Published in print: Sep 1, 2024
Discussion open until: Dec 9, 2024
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence (AI)
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Correlation
- Data analysis
- Dynamics (solid mechanics)
- Education
- Engineering fundamentals
- Engineering mechanics
- Equations of motion
- Highway transportation
- Infrastructure
- Mathematical functions
- Mathematics
- Matrix (mathematics)
- Methodology (by type)
- Neural networks
- Practice and Profession
- Research methods (by type)
- Solid mechanics
- Statistics
- Training
- Transportation engineering
- Vehicles
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