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    Reinforcement Metalearning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop

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    Name:
    6_DOF_Intercept_JSR_rev1.pdf
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    Format:
    PDF
    Description:
    Final Accepted Manuscript
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    Author
    Gaudet, Brian
    Furfaro, Roberto
    Linares, Richard
    Scorsoglio, Andrea
    Affiliation
    University of Arizona, Department of Systems and Industrial Engineering
    University of Arizona, Department of Aerospace and Mechanical Engineering
    Issue Date
    2020-11-24
    
    Metadata
    Show full item record
    Publisher
    AIAA International
    Citation
    Gaudet, B., Furfaro, R., Linares, R., & Scorsoglio, A. (2021). Reinforcement Metalearning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop. Journal of Spacecraft and Rockets, 58(2), 386-399.
    Journal
    Journal of Spacecraft and Rockets
    Rights
    Copyright © 2020 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    This Paper uses Reinforcement Meta-Learning to optimize an adaptive integrated guidance, navigation, and control system suitable for exoatmospheric interception of a maneuvering target. The system maps observations consisting of strapdown seeker angles and rate gyroscope measurements directly to thruster on/off commands. Using a high fidelity six-degree-of-freedom simulator, this Paper demonstrates that the optimized policy can adapt to parasitic effects including seeker angle measurement lag, thruster control lag, the parasitic attitude loop resulting from scale factor errors and Gaussian noise on angle and rotational velocity measurements, and a time-varying center of mass caused by fuel consumption and slosh. Importantly, the optimized policy gives good performance over a wide range of challenging target maneuvers. Unlike previous work that enhances range observability by inducing line of sight oscillations, this Paper’s system is optimized to use only measurements available from the seeker and rate gyros. Through extensive Monte Carlo simulation of randomized exoatmospheric interception scenarios, this Paper demonstrates that the optimized policy gives performance close to that of augmented proportional navigation with perfect knowledge of the full engagement state. The optimized system is computationally efficient and requires minimal memory and should be compatible with today’s flight processors.
    ISSN
    0022-4650
    EISSN
    1533-6794
    DOI
    10.2514/1.a34841
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.2514/1.a34841
    Scopus Count
    Collections
    UA Faculty Publications

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