[en] Recent work on black-box polynomial nonlinear state-space modeling for hysteresis identification has provided promising results, but struggles with a large number of parameters due to the use of multivariate polynomials. This drawback is tackled in the current paper by applying a decoupling approach that results in a more parsimonious representation involving univariate polynomials. This work is carried out numerically on input-output data generated by a Bouc-Wen hysteretic model and follows up on earlier work of the authors. The current article discusses the polynomial decoupling approach and explores the selection of the number of univariate polynomials with the polynomial degree. We have found that the presented decoupling approach is able to reduce the number of parameters of the full nonlinear model up to about 50%, while maintaining a comparable output error level.
Disciplines :
Aerospace & aeronautics engineering
Author, co-author :
Esfahani, Alireza; Vrije Universiteit Brussel - VUB > ELEC Department
Dreesen, Philippe; Vrije Universiteit Brussel - VUB > ELEC Department
Tiels, Koen; Vrije Universiteit Brussel - VUB > ELEC Department
Noël, Jean-Philippe ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Laboratoire de structures et systèmes spatiaux
Schoukens, Johan; Vrije Universiteit Brussel - VUB > ELEC Department
Language :
English
Title :
Parameter reduction in nonlinear state-space identification of hysteresis
Publication date :
May 2018
Journal title :
Mechanical Systems and Signal Processing
ISSN :
0888-3270
eISSN :
1096-1216
Publisher :
Elsevier, Atlanta, United States
Volume :
104
Pages :
884-895
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
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