Artificial neural networks in Space Station optimal attitude control

Innovative techniques of using “artificial neural networks” (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using control moment gyros (CMGs) are investigated. The first technique uses a feed-forward ANN with multi-layer perceptrons to obtain an...

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Bibliographic Details
Published inActa astronautica Vol. 35; no. 2; pp. 107 - 117
Main Authors Kumar, Renjith R., Seywald, Hans, Deshpande, Samir M., Rahman, Zia
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 1995
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Summary:Innovative techniques of using “artificial neural networks” (ANN) for improving the performance of the pitch axis attitude control system of Space Station Freedom using control moment gyros (CMGs) are investigated. The first technique uses a feed-forward ANN with multi-layer perceptrons to obtain an on-line controller which improves the performance of the control system via a model following approach. The second technique uses a single layer feed-forward ANN with a modified back propagation scheme to estimate the internal plant variations and the external disturbances separately. These estimates are then used to solve two differential Riccati equations to obtain time varying gains which improve the control system performance in successive orbits.
ISSN:0094-5765
1879-2030
DOI:10.1016/0094-5765(94)00153-D