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Model Order Reduction: A Comparison between Integer and Non-Integer Order Systems Approaches
In this paper, classical and non-integer model order reduction methodologies are compared. Non integer order calculus has been used to generalize many classical control strategies. The property of compressing information in modelling systems, distributed in time and space, and the capability of desc...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515405/ http://dx.doi.org/10.3390/e21090876 |
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author | Caponetto, Riccardo Machado, José Tenreiro Murgano, Emanuele Xibilia, Maria Gabriella |
author_facet | Caponetto, Riccardo Machado, José Tenreiro Murgano, Emanuele Xibilia, Maria Gabriella |
author_sort | Caponetto, Riccardo |
collection | PubMed |
description | In this paper, classical and non-integer model order reduction methodologies are compared. Non integer order calculus has been used to generalize many classical control strategies. The property of compressing information in modelling systems, distributed in time and space, and the capability of describing long-term memory effects in dynamical systems are two features suggesting also the application of fractional calculus in model order reduction. In the paper, an open loop balanced realization is compared with three approaches based on a non-integer representation of the reduced system. Several case studies are considered and compared. The results confirm the capability of fractional order systems to capture and compress the dynamics of high order systems. |
format | Online Article Text |
id | pubmed-7515405 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75154052020-11-09 Model Order Reduction: A Comparison between Integer and Non-Integer Order Systems Approaches Caponetto, Riccardo Machado, José Tenreiro Murgano, Emanuele Xibilia, Maria Gabriella Entropy (Basel) Article In this paper, classical and non-integer model order reduction methodologies are compared. Non integer order calculus has been used to generalize many classical control strategies. The property of compressing information in modelling systems, distributed in time and space, and the capability of describing long-term memory effects in dynamical systems are two features suggesting also the application of fractional calculus in model order reduction. In the paper, an open loop balanced realization is compared with three approaches based on a non-integer representation of the reduced system. Several case studies are considered and compared. The results confirm the capability of fractional order systems to capture and compress the dynamics of high order systems. MDPI 2019-09-09 /pmc/articles/PMC7515405/ http://dx.doi.org/10.3390/e21090876 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Caponetto, Riccardo Machado, José Tenreiro Murgano, Emanuele Xibilia, Maria Gabriella Model Order Reduction: A Comparison between Integer and Non-Integer Order Systems Approaches |
title | Model Order Reduction: A Comparison between Integer and Non-Integer Order Systems Approaches |
title_full | Model Order Reduction: A Comparison between Integer and Non-Integer Order Systems Approaches |
title_fullStr | Model Order Reduction: A Comparison between Integer and Non-Integer Order Systems Approaches |
title_full_unstemmed | Model Order Reduction: A Comparison between Integer and Non-Integer Order Systems Approaches |
title_short | Model Order Reduction: A Comparison between Integer and Non-Integer Order Systems Approaches |
title_sort | model order reduction: a comparison between integer and non-integer order systems approaches |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7515405/ http://dx.doi.org/10.3390/e21090876 |
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