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

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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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https://doi.org/10.1016/j.conengprac.2019.104165
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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

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Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

A. Padilla
H. Garnier
P.C. Young
F. Chen
J.I. Yuz

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Article number	104165
<mark>Journal publication date</mark>	1/12/2019
<mark>Journal</mark>	Control Engineering Practice
Volume	93
Number of pages	12
Publication Status	Published
Early online date	9/10/19
<mark>Original language</mark>	English

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.

Bibliographic note

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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