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Attitude control of spacecraft using neural networksThis paper investigates the use of radial basis function neural networks for adaptive attitude control and momentum management of spacecraft. In the first part of the paper, neural networks are trained to learn from a family of open-loop optimal controls parameterized by the initial states and times-to-go. The trained is then used for closed-loop control. In the second part of the paper, neural networks are used for direct adaptive control in the presence of unmodeled effects and parameter uncertainty. The control and learning laws are derived using the method of Lyapunov.
Document ID
19950049762
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Vadali, Srinivas R.
(Texas A&M Univ. College Station, TX, US, United States)
Krishnan, S.
(Texas A&M Univ. College Station, TX, US, United States)
Singh, T.
(Texas A&M Univ. College Station, TX, US, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1993
Publication Information
Publication: In: Spaceflight mechanics, 1993; AAS(AIAA Spaceflight Mechanics Meeting, 3rd, Pasadena, CA, Feb. 22-24, 1993, Parts 1 & 2 . A95-81344
Publisher: American Astronautical Society (Advances in the Astronautical Sciences, Vol. 82, Pts. 1 & 2)
ISSN: 0065-3438
Subject Category
Spacecraft Design, Testing And Performance
Accession Number
95A81361
Funding Number(s)
CONTRACT_GRANT: NAS1-19373
Distribution Limits
Public
Copyright
Other

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