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  • Padillaetal_2019

    Rights statement: This is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 93, 2019 DOI: 10.1016/j.conengprac.2019.104165

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Identification of continuous-time models with slowly time-varying parameters

Research output: Contribution to journalJournal articlepeer-review

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Identification of continuous-time models with slowly time-varying parameters. / Padilla, A.; Garnier, H.; Young, P.C.; Chen, F.; Yuz, J.I.

In: Control Engineering Practice, Vol. 93, 104165, 01.12.2019.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Padilla, A, Garnier, H, Young, PC, Chen, F & Yuz, JI 2019, 'Identification of continuous-time models with slowly time-varying parameters', Control Engineering Practice, vol. 93, 104165. https://doi.org/10.1016/j.conengprac.2019.104165

APA

Padilla, A., Garnier, H., Young, P. C., Chen, F., & Yuz, J. I. (2019). Identification of continuous-time models with slowly time-varying parameters. Control Engineering Practice, 93, [104165]. https://doi.org/10.1016/j.conengprac.2019.104165

Vancouver

Padilla A, Garnier H, Young PC, Chen F, Yuz JI. Identification of continuous-time models with slowly time-varying parameters. Control Engineering Practice. 2019 Dec 1;93. 104165. https://doi.org/10.1016/j.conengprac.2019.104165

Author

Padilla, A. ; Garnier, H. ; Young, P.C. ; Chen, F. ; Yuz, J.I. / Identification of continuous-time models with slowly time-varying parameters. In: Control Engineering Practice. 2019 ; Vol. 93.

Bibtex

@article{3795227bc6bb414ea6e4034ffad550a4,
title = "Identification of continuous-time models with slowly time-varying parameters",
abstract = "The off-line estimation of the parameters of continuous-time, linear, time-invariant transfer function models can be achieved straightforwardly using linear prefilters on the measured input and output of the system. The on-line estimation of continuous-time models with time-varying parameters is less straightforward because it requires the updating of the continuous-time prefilter parameters. This paper shows how such on-line estimation is possible by using recursive instrumental variable approaches. The proposed methods are presented in detail and also evaluated on a numerical example using both single experiment and Monte Carlo simulation analysis. In addition, the proposed recursive algorithms are tested using data from two real-life systems. ",
keywords = "Continuous-time model identification, Instrumental variable method, Linear filter methods, Linear time-varying system, Recursive methods, Continuous time systems, Intelligent systems, Monte Carlo methods, Numerical methods, Time varying control systems, Continuous time modeling, Instrumental variable methods, Linear filter method, Linear time-varying systems, Parameter estimation",
author = "A. Padilla and H. Garnier and P.C. Young and F. Chen and J.I. Yuz",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 93, 2019 DOI: 10.1016/j.conengprac.2019.104165",
year = "2019",
month = dec,
day = "1",
doi = "10.1016/j.conengprac.2019.104165",
language = "English",
volume = "93",
journal = "Control Engineering Practice",
issn = "0967-0661",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - Identification of continuous-time models with slowly time-varying parameters

AU - Padilla, A.

AU - Garnier, H.

AU - Young, P.C.

AU - Chen, F.

AU - Yuz, J.I.

N1 - This is the author’s version of a work that was accepted for publication in Control Engineering Practice. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Control Engineering Practice, 93, 2019 DOI: 10.1016/j.conengprac.2019.104165

PY - 2019/12/1

Y1 - 2019/12/1

N2 - The off-line estimation of the parameters of continuous-time, linear, time-invariant transfer function models can be achieved straightforwardly using linear prefilters on the measured input and output of the system. The on-line estimation of continuous-time models with time-varying parameters is less straightforward because it requires the updating of the continuous-time prefilter parameters. This paper shows how such on-line estimation is possible by using recursive instrumental variable approaches. The proposed methods are presented in detail and also evaluated on a numerical example using both single experiment and Monte Carlo simulation analysis. In addition, the proposed recursive algorithms are tested using data from two real-life systems.

AB - The off-line estimation of the parameters of continuous-time, linear, time-invariant transfer function models can be achieved straightforwardly using linear prefilters on the measured input and output of the system. The on-line estimation of continuous-time models with time-varying parameters is less straightforward because it requires the updating of the continuous-time prefilter parameters. This paper shows how such on-line estimation is possible by using recursive instrumental variable approaches. The proposed methods are presented in detail and also evaluated on a numerical example using both single experiment and Monte Carlo simulation analysis. In addition, the proposed recursive algorithms are tested using data from two real-life systems.

KW - Continuous-time model identification

KW - Instrumental variable method

KW - Linear filter methods

KW - Linear time-varying system

KW - Recursive methods

KW - Continuous time systems

KW - Intelligent systems

KW - Monte Carlo methods

KW - Numerical methods

KW - Time varying control systems

KW - Continuous time modeling

KW - Instrumental variable methods

KW - Linear filter method

KW - Linear time-varying systems

KW - Parameter estimation

U2 - 10.1016/j.conengprac.2019.104165

DO - 10.1016/j.conengprac.2019.104165

M3 - Journal article

VL - 93

JO - Control Engineering Practice

JF - Control Engineering Practice

SN - 0967-0661

M1 - 104165

ER -