An empirical study on decoupling PNLSS models illustrated on an airplane

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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.

Original languageEnglish
Title of host publicationProceedings of the 19th IFAC Symposium on System Identification SYSID 2021
PublisherIFAC - PapersOnLine
Number of pages6
ISBN (Electronic)2405-8963
Publication statusPublished - 1 Jul 2021
Event19th IFAC Symposium on System Identification SYSID 2021 -
Duration: 13 Jul 202116 Jul 2021

Publication series

ISSN (Print)2405-8963


Conference19th IFAC Symposium on System Identification SYSID 2021
Internet address

Bibliographical note

Funding Information:
This work was funded by the Strategic Research Program SRP60 of the Vrije Universiteit Brussel.

Publisher Copyright:
© 2021 The Authors.

Copyright 2021 Elsevier B.V., All rights reserved.


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