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

Joannes Franciscus Schoukens; Mark Vaes; Rik Pintelon

doi:10.1109/MCS.2016.2535918

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

Joannes Franciscus Schoukens, Mark Vaes, Rik Pintelon

Research output: Contribution to journal › Article › peer-review

75 Citations (Scopus)

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

Original language	English
Pages (from-to)	38-69
Journal	IEEE Control Systems Magazine
Volume	36
Issue number	3
DOIs	https://doi.org/10.1109/MCS.2016.2535918
Publication status	Published - 1 Jun 2016

Keywords

Data models
Distortion measurement
Frequency measurement
Linear systems
Nonlinear distortion
Nonlinear systems
Uncertainty

Access to Document

10.1109/MCS.2016.2535918

1 Finished

DWTC282: Dynamical systems, control and optimization
Pintelon, R., Vandewalle, J., Aeyels, D., Sepulchre, R., Kinnaert, M., Vande Wouwer, A., Blondel, V., Winkin, J., Boyd, S. & Leonard, N.
1/04/12 → 30/09/17
Project: Fundamental

Cite this

@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 = jun,

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",

}

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 -

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

Abstract

Keywords

Access to Document

Fingerprint

Projects

DWTC282: Dynamical systems, control and optimization

Cite this