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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

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Article number104165
<mark>Journal publication date</mark>1/12/2019
<mark>Journal</mark>Control Engineering Practice
Volume93
Number of pages12
Publication StatusPublished
Early online date9/10/19
<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