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This paper illustrates a combined nonparametric and parametric system identification framework for modeling nonlinear vibrating structures. First step is the analysis: multiple-input multiple-output measurements are (semi-automatically) preprocessed, and a nonparametric Best Linear Approximation (BLA) method is performed. The outcome of the BLA analysis results in nonparametric frequency response function, noise and nonlinear distortion estimates. Based on this information, a linear parametric (state-space) model is built. This model is used to initialize a high complexity Polynomial Nonlinear State-Space PNLSS model. The nonlinear part of a PNLSS model is manifested as a combination of high-dimensional multivariate polynomials. The last step in the proposed approach is the decoupling: transforming multivariate polynomials into a simplified, alternative basis, thereby dramatically reducing the number of parameters. In this work a novel filtered canonical polyadic decomposition (CPD) is used. The proposed methodology is illustrated on, but of course not limited to, a ground vibration testing measurement of an air fighter.
|Title of host publication||Proceedings of the 19th IFAC Symposium on System Identification SYSID 2021|
|Publisher||IFAC - PapersOnLine|
|Number of pages||6|
|Publication status||Published - 1 Jul 2021|
|Event||19th IFAC Symposium on System Identification SYSID 2021 - |
Duration: 13 Jul 2021 → 16 Jul 2021
|Conference||19th IFAC Symposium on System Identification SYSID 2021|
|Period||13/07/21 → 16/07/21|
Bibliographical noteFunding Information:
This work was funded by the Strategic Research Program SRP60 of the Vrije Universiteit Brussel.
© 2021 The Authors.
Copyright 2021 Elsevier B.V., All rights reserved.
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SRP60: SRP-Groeifinanciering: A system identification framework for multi-fidelity modelling
De Troyer, T., Runacres, M., Blondeau, J., Bram, S., Bellemans, A. & Contino, F.
1/03/19 → 29/02/24