Fractional order model identification: Computational optimization

© 2015 IEEE.This paper deals with the identification of fractional models with one fractional parameter. This kind of models are capable to represent an extensive range of dynamics, including overdamped and oscillatory behaviors. The identification algorithm consists in applying an optimization func...

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Main Authors: Guevara, E., Meneses, H., Arrieta, O., Vilanova, R., Visioli, A., Padula, Fabrizio
Format: Conference Paper
Published: 2015
Online Access:http://hdl.handle.net/20.500.11937/52789
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author Guevara, E.
Meneses, H.
Arrieta, O.
Vilanova, R.
Visioli, A.
Padula, Fabrizio
author_facet Guevara, E.
Meneses, H.
Arrieta, O.
Vilanova, R.
Visioli, A.
Padula, Fabrizio
author_sort Guevara, E.
building Curtin Institutional Repository
collection Online Access
description © 2015 IEEE.This paper deals with the identification of fractional models with one fractional parameter. This kind of models are capable to represent an extensive range of dynamics, including overdamped and oscillatory behaviors. The identification algorithm consists in applying an optimization function, starting from an initial point, that allows the program to calculate a very representative model of the process. The results demonstrate the usefulness and robustness of the tool, which can be employed to identify integer and fractional systems in an easy way and this can be later exploited for further studies, for example the development of tuning rules.
first_indexed 2025-11-14T09:53:06Z
format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T09:53:06Z
publishDate 2015
recordtype eprints
repository_type Digital Repository
spelling curtin-20.500.11937-527892017-09-13T15:39:42Z Fractional order model identification: Computational optimization Guevara, E. Meneses, H. Arrieta, O. Vilanova, R. Visioli, A. Padula, Fabrizio © 2015 IEEE.This paper deals with the identification of fractional models with one fractional parameter. This kind of models are capable to represent an extensive range of dynamics, including overdamped and oscillatory behaviors. The identification algorithm consists in applying an optimization function, starting from an initial point, that allows the program to calculate a very representative model of the process. The results demonstrate the usefulness and robustness of the tool, which can be employed to identify integer and fractional systems in an easy way and this can be later exploited for further studies, for example the development of tuning rules. 2015 Conference Paper http://hdl.handle.net/20.500.11937/52789 10.1109/ETFA.2015.7301630 restricted
spellingShingle Guevara, E.
Meneses, H.
Arrieta, O.
Vilanova, R.
Visioli, A.
Padula, Fabrizio
Fractional order model identification: Computational optimization
title Fractional order model identification: Computational optimization
title_full Fractional order model identification: Computational optimization
title_fullStr Fractional order model identification: Computational optimization
title_full_unstemmed Fractional order model identification: Computational optimization
title_short Fractional order model identification: Computational optimization
title_sort fractional order model identification: computational optimization
url http://hdl.handle.net/20.500.11937/52789