Parameter reduction in nonlinear state-space identification of hysteresis. (1st May 2018)

• Record Type: Journal Article
• Authors: Fakhrizadeh Esfahani, Alireza; Dreesen, Philippe; Tiels, Koen; Noël, Jean-Philippe; Schoukens, Johan
• Is Part Of: Mechanical systems and signal processing. Volume 104(2018)
• Page Start: 884
• Page End: 895
• Publication Date: 2018-05-01
• Subjects: Polynomial nonlinear state-space -- Hysteretic system -- Bouc-Wen -- Tensor decomposition -- Canonical polyadic decomposition -- Decoupling multivariate polynomials; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.10.017
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Estimation of a polynomial state space model of a Bouc-Wen hysteretic system. Simplification of the polynomial state space model using a decoupling method. Comparison of a large range of decoupled models for the Bouc-Wen system. All estimated models are successfully validated on 2 independent validation datasets. Discussion of the trade-off of the complexity and the precision of the models. Abstract: 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.