Abstract
The abort mission refers to the mission where the landing vehicle needs to terminate the landing mission when an anomaly happens and be safely guided to the desired orbit. This paper focuses on solving the time-optimal abort guidance (TOAG) problem in real-time via the feature-based learning method. First, according to the optimal control theory, the features are identified to represent the optimal solutions of TOAG using a few parameters. After that, a sufficiently large data set of time-optimal abort trajectories is generated offline by solving the TOAG problems with different initial conditions. Then, the features are extracted for all generated cases. To find the implicit relationships between the initial conditions and identified features, neural networks are constructed to map the relationships based on the generated data set. Finally, experimental flight tests are conducted to demonstrate the onboard computation capability and effectiveness of the proposed method.
Recommended Citation
V. Kenny et al., "Optimal Abort Guidance and Experimental Verification based on Feature Learning," Journal of Aerospace Engineering, vol. 37, no. 2, article no. 04023124, American Society of Civil Engineers, Mar 2024.
The definitive version is available at https://doi.org/10.1061/JAEEEZ.ASENG-5030
Department(s)
Mechanical and Aerospace Engineering
International Standard Serial Number (ISSN)
1943-5525; 0893-1321
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 American Society of Civil Engineers, All rights reserved.
Publication Date
01 Mar 2024
