Machine learning assisted low-thrust orbit-raising: a comparative assessment of a sequential algorithm and a deep reinforcement learning approach
Dutta, Atri Arustei, Adrian Chace, Matthew Chadalavada, Pardhasai Steck, James E. Zaidi, Talha Munir, Arslan
Dutta, Atri
Arustei, Adrian
Chace, Matthew
Chadalavada, Pardhasai
Steck, James E.
Zaidi, Talha
Munir, Arslan
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Time Period
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Digitization Date
Issue Date
2024-01-04
Type
Conference paper
Genre
Keywords
Reinforcement learning,Thrust,Geosynchronous equatorial orbit,Artificial neural network,Computing,Numerical simulation,Optimal control problem,Solar electric propulsion,Propellant,Dynamic modelling
Subjects (LCSH)
Citation
Atri Dutta, Adrian Arustei, Matthew Chace, Pardhasai Chadalavada, James Steck, Talha Zaidi and Arslan Munir. "Machine Learning Assisted Low-Thrust Orbit-Raising: A Comparative Assessment of a Sequential Algorithm and Deep Reinforcement Learning Approach," AIAA 2024-1669. AIAA SCITECH 2024 Forum. January 2024.
Abstract
The focus of the paper is machine-learning-assisted computation of low-thrust orbit-raising trajectories. We consider a sequential algorithm for computing multi-revolution trajectories, whose optimization cost function parameters can be updated through a high-level planner utilizing a suitably trained artificial neural network. Considering two different orbit-raising mission scenarios based on the final target orbit (geostationary and near-rectilinear halo orbit), we conduct numerical simulations to compare the results of this approach with that provided by a deep reinforcement learning framework. © 2024 by Atri Dutta, Adrian Arustei, Matthew Chace, Pardhasai Chadalavada, James Steck, Talha Zaidi, Arslan Munir. Published by the American Institute of Aeronautics and Astronautics, Inc.
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Description
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publisher
American Institute of Aeronautics and Astronautics Inc, AIAA
Journal
Book Title
Series
AIAA SciTech Forum and Exposition, 2024
8 January 2024 through 12 January 2024
Orlando
310199
8 January 2024 through 12 January 2024
Orlando
310199