Abstract
The present study exploits the maximum likelihood identification framework for deriving statistically-optimal models of nonlinear mechanical systems. The identification problem is formulated in the frequency domain, and model parameters are calculated by minimising a weighted least-squares cost function. Initial values of the model parameters are obtained by means of a nonlinear subspace algorithm. The complete identification methodology is first demonstrated on a Duffing oscillator, prior to being applied to a full-scale aerospace structure.
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Acknowledgements
The author J.P. Noël is a Research Fellow (FRIA fellowship) of the Fonds de la Recherche Scientifique—FNRS which is gratefully acknowledged.
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© 2014 The Society for Experimental Mechanics, Inc.
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Noël, J.P., Schoukens, J., Kerschen, G. (2014). A Stochastic Framework for Subspace Identification of a Strongly Nonlinear Aerospace Structure. In: Kerschen, G. (eds) Nonlinear Dynamics, Volume 2. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-04522-1_16
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DOI: https://doi.org/10.1007/978-3-319-04522-1_16
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