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Parameter estimation in space systems using recurrent neural networksThe identification of time-varying parameters encountered in space systems is addressed, using artificial neural systems. A hybrid feedforward/feedback neural network, namely a recurrent multilayer perception, is used as the model structure in the nonlinear system identification. The feedforward portion of the network architecture provides its well-known interpolation property, while through recurrency and cross-talk, the local information feedback enables representation of temporal variations in the system nonlinearities. The standard back-propagation-learning algorithm is modified and it is used for both the off-line and on-line supervised training of the proposed hybrid network. The performance of recurrent multilayer perceptron networks in identifying parameters of nonlinear dynamic systems is investigated by estimating the mass properties of a representative large spacecraft. The changes in the spacecraft inertia are predicted using a trained neural network, during two configurations corresponding to the early and late stages of the spacecraft on-orbit assembly sequence. The proposed on-line mass properties estimation capability offers encouraging results, though, further research is warranted for training and testing the predictive capabilities of these networks beyond nominal spacecraft operations.
Document ID
Document Type
Conference Paper
Parlos, Alexander G.
(Texas A&M Univ. College Station, TX, United States)
Atiya, Amir F.
(Texas A & M University College Station, United States)
Sunkel, John W.
(NASA Johnson Space Center Houston, TX, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1991
Subject Category
Report/Patent Number
AIAA PAPER 91-2716
Meeting Information
AIAA Guidance, Navigation and Control Conference(New Orleans, LA)
Accession Number
Funding Number(s)
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