Abstract
Nonlinear energy sink (NES) devices have recently been introduced in civil engineering for structural control. Because of the essential geometric nonlinearities governing these devices, identification must be performed in the time domain. Such methods can be challenging due to processing requirements, sensitivity to noise, and the presence of nonlinearity. Bayesian analysis methods have been shown to overcome these challenges, providing robust identification of nonlinear models. In this study we compare the unscented Kalman filter and the particle filter for the identification of a prototype NES device. Simulated responses developed using a device model and a sample set of parameters are used here to demonstrate and evaluate the identification process. Analysis of the identification results is conducted by varying the identification technique used and the selection of the prior distributions on the parameters. These preliminary numerical results will inform a later implementation on experimental response data.
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Acknowledgements
This work is partially supported by the National Science Foundation (NSF) under Grant No. DGE-1333468. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. The authors also acknowledge Christian Silva, who designed and constructed this NES device.
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Lund, A., Dyke, S.J., Song, W., Bilionis, I. (2020). Bayesian Identification of a Nonlinear Energy Sink Device: Method Comparison. In: Barthorpe, R. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-030-12075-7_19
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DOI: https://doi.org/10.1007/978-3-030-12075-7_19
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