Identification of linear systems with nonlinear distortions

J. Schoukens, R. Pintelon, T. Dobrowiecki, Y. Rolain

Research output: Contribution to journalArticle

165 Citations (Scopus)

Abstract

This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.

Original languageEnglish
Pages (from-to)491-504
Number of pages14
JournalAutomatica
Volume41
Issue number3
DOIs
Publication statusPublished - Mar 2005

Fingerprint

Nonlinear distortion
Linear systems
Identification (control systems)
Nonlinear systems
Experiments

Keywords

  • Linear system
  • Nonlinear system
  • System identification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Identification of linear systems with nonlinear distortions. / Schoukens, J.; Pintelon, R.; Dobrowiecki, T.; Rolain, Y.

In: Automatica, Vol. 41, No. 3, 03.2005, p. 491-504.

Research output: Contribution to journalArticle

Schoukens, J. ; Pintelon, R. ; Dobrowiecki, T. ; Rolain, Y. / Identification of linear systems with nonlinear distortions. In: Automatica. 2005 ; Vol. 41, No. 3. pp. 491-504.
@article{9d9b6f21390840489c2faf7989974674,
title = "Identification of linear systems with nonlinear distortions",
abstract = "This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.",
keywords = "Linear system, Nonlinear system, System identification",
author = "J. Schoukens and R. Pintelon and T. Dobrowiecki and Y. Rolain",
year = "2005",
month = "3",
doi = "10.1016/j.automatica.2004.10.004",
language = "English",
volume = "41",
pages = "491--504",
journal = "Automatica",
issn = "0005-1098",
publisher = "Elsevier Limited",
number = "3",

}

TY - JOUR

T1 - Identification of linear systems with nonlinear distortions

AU - Schoukens, J.

AU - Pintelon, R.

AU - Dobrowiecki, T.

AU - Rolain, Y.

PY - 2005/3

Y1 - 2005/3

N2 - This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.

AB - This paper studies the impact of nonlinear distortions on linear system identification. It collects a number of previously published methods in a fully integrated approach to measure and model these systems from experimental data. First a theoretical framework is proposed that extends the linear system description to include the impact of nonlinear distortions: the nonlinear system is replaced by a linear model plus a 'nonlinear noise source'. The class of nonlinear systems covered by this approach is described and the properties of the extended linear representation are studied. These results are used to design the experiments; to detect the level of the nonlinear distortions; to measure efficiently the 'best' linear approximation; to reveal the even or odd nature of the nonlinearity; to identify a parametric linear model; and to improve the model selection procedures in the presence of nonlinear distortions.

KW - Linear system

KW - Nonlinear system

KW - System identification

UR - http://www.scopus.com/inward/record.url?scp=13244295720&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=13244295720&partnerID=8YFLogxK

U2 - 10.1016/j.automatica.2004.10.004

DO - 10.1016/j.automatica.2004.10.004

M3 - Article

AN - SCOPUS:13244295720

VL - 41

SP - 491

EP - 504

JO - Automatica

JF - Automatica

SN - 0005-1098

IS - 3

ER -