Show simple item record

dc.contributor.authorGaudet, Brian
dc.contributor.authorFurfaro, Roberto
dc.contributor.authorLinares, Richard
dc.contributor.authorScorsoglio, Andrea
dc.date.accessioned2021-04-08T02:03:05Z
dc.date.available2021-04-08T02:03:05Z
dc.date.issued2020-11-24
dc.identifier.citationGaudet, 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.en_US
dc.identifier.issn0022-4650
dc.identifier.doi10.2514/1.a34841
dc.identifier.urihttp://hdl.handle.net/10150/657645
dc.description.abstractThis 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.en_US
dc.language.isoenen_US
dc.publisherAIAA Internationalen_US
dc.rightsCopyright © 2020 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.titleReinforcement Metalearning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loopen_US
dc.typeArticleen_US
dc.identifier.eissn1533-6794
dc.contributor.departmentUniversity of Arizona, Department of Systems and Industrial Engineeringen_US
dc.contributor.departmentUniversity of Arizona, Department of Aerospace and Mechanical Engineeringen_US
dc.identifier.journalJournal of Spacecraft and Rocketsen_US
dc.description.collectioninformationThis 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.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleJournal of Spacecraft and Rockets
dc.source.volume58
dc.source.issue2
dc.source.beginpage386
dc.source.endpage399
refterms.dateFOA2021-04-08T02:03:06Z


Files in this item

Thumbnail
Name:
6_DOF_Intercept_JSR_rev1.pdf
Size:
2.153Mb
Format:
PDF
Description:
Final Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record