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Model reduction for the dynamics and control of large structural systems via neutral network processing direct numerical optimizationThree neural network processing approaches in a direct numerical optimization model reduction scheme are proposed and investigated. Large structural systems, such as large space structures, offer new challenges to both structural dynamicists and control engineers. One such challenge is that of dimensionality. Indeed these distributed parameter systems can be modeled either by infinite dimensional mathematical models (typically partial differential equations) or by high dimensional discrete models (typically finite element models) often exhibiting thousands of vibrational modes usually closely spaced and with little, if any, damping. Clearly, some form of model reduction is in order, especially for the control engineer who can actively control but a few of the modes using system identification based on a limited number of sensors. Inasmuch as the amount of 'control spillover' (in which the control inputs excite the neglected dynamics) and/or 'observation spillover' (where neglected dynamics affect system identification) is to a large extent determined by the choice of particular reduced model (RM), the way in which this model reduction is carried out is often critical.
Document ID
19930009631
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Becus, Georges A.
(Cincinnati Univ. OH, United States)
Chan, Alistair K.
(Cincinnati Univ. OH, United States)
Date Acquired
September 6, 2013
Publication Date
February 1, 1993
Publication Information
Publication: NASA. Langley Research Center, The Fifth NASA(DOD Controls-Structures Interaction Technology Conference, Part 1
Subject Category
Spacecraft Design, Testing And Performance
Accession Number
93N18820
Funding Number(s)
CONTRACT_GRANT: NAG3-1174
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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