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Linear System Identification in a Nonlinear Setting: Nonparametric Analysis of the Nonlinear Distortions and Their Impact on the Best Linear Approximation. / Schoukens, Joannes Franciscus; Vaes, Mark; Pintelon, Rik.

In: IEEE Control Systems Magazine, Vol. 36, No. 3, 01.06.2016, p. 38-69.

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@article{65588749cf844566890b9d52fb3c6565,
title = "Linear System Identification in a Nonlinear Setting: Nonparametric Analysis of the Nonlinear Distortions and Their Impact on the Best Linear Approximation.",
abstract = "Linear system identification [1]?[4] is a basic step in modern control design approaches. Starting from experimental data, a linear dynamic time-invariant model is identified to describe the relationship between the reference signal and the output of the system. At the same time, the power spectrum of the unmodeled disturbances is identified to generate uncertainty bounds on the estimated",
keywords = "Data models, Distortion measurement, Frequency measurement, Linear systems, Nonlinear distortion, Nonlinear systems, Uncertainty",
author = "Schoukens, {Joannes Franciscus} and Mark Vaes and Rik Pintelon",
year = "2016",
month = "6",
day = "1",
doi = "10.1109/MCS.2016.2535918",
language = "English",
volume = "36",
pages = "38--69",
journal = "IEEE Control Systems Magazine",
issn = "1066-033X",
publisher = "IEEE",
number = "3",

}

RIS

TY - JOUR

T1 - Linear System Identification in a Nonlinear Setting: Nonparametric Analysis of the Nonlinear Distortions and Their Impact on the Best Linear Approximation.

AU - Schoukens, Joannes Franciscus

AU - Vaes, Mark

AU - Pintelon, Rik

PY - 2016/6/1

Y1 - 2016/6/1

N2 - Linear system identification [1]?[4] is a basic step in modern control design approaches. Starting from experimental data, a linear dynamic time-invariant model is identified to describe the relationship between the reference signal and the output of the system. At the same time, the power spectrum of the unmodeled disturbances is identified to generate uncertainty bounds on the estimated

AB - Linear system identification [1]?[4] is a basic step in modern control design approaches. Starting from experimental data, a linear dynamic time-invariant model is identified to describe the relationship between the reference signal and the output of the system. At the same time, the power spectrum of the unmodeled disturbances is identified to generate uncertainty bounds on the estimated

KW - Data models

KW - Distortion measurement

KW - Frequency measurement

KW - Linear systems

KW - Nonlinear distortion

KW - Nonlinear systems

KW - Uncertainty

U2 - 10.1109/MCS.2016.2535918

DO - 10.1109/MCS.2016.2535918

M3 - Article

VL - 36

SP - 38

EP - 69

JO - IEEE Control Systems Magazine

JF - IEEE Control Systems Magazine

SN - 1066-033X

IS - 3

ER -

ID: 24706310