Guidance for Closed-Loop Transfers using Reinforcement Learning with Application to Libration Point Orbits
Name
2020_scitech07.pdf
Description
Accepted version
Size
1.6 MB
Format
Adobe PDF
Checksum (MD5)
747f9036ce0f68a18e9f4cf2a04afd1b
Author(s)
LaFarge, Nicholas B
Miller, Daniel
Howell, Kathleen C
Linares, Richard
Date Issued
2020
Journal
AIAA Scitech 2020 Forum
Publisher
American Institute of Aeronautics and Astronautics (AIAA)
Citation
LaFarge, Nicholas B, Miller, Daniel, Howell, Kathleen C and Linares, Richard. 2020. "Guidance for Closed-Loop Transfers using Reinforcement Learning with Application to Libration Point Orbits." AIAA Scitech 2020 Forum, 1 PartF.
Version
Author's final manuscript
Abstract
While human presence in cislunar space continues to expand, so too does the demand for ‘lightweight’ automated on-board processes. In nonlinear dynamical environments, computationally efficient guidance strategies are challenging. Many traditional approaches rely on either simplifying assumptions in the dynamical model or abundant computational resources. The proposed controller employs the use of the nonlinear equations of motion without imposing a heavy workload on a flight computer. The guidance framework is nevertheless able to leverage high-performance computing by separating the training from the resulting controller. Practical examples demonstrate the flexibility of a reinforcement learning approach, and suggest extendability to higher-fidelity domains.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.2514/6.2020-0458
