A New Fractional Reduced-Order Model-Inspired System Identification Method for Dynamical Systems
This paper presents a new method for identifying dynamical systems to get fractional-reduced-order models based on the process reaction curve. This proposal uses information collected from the process. It can be applied to processes with an S-shaped step response that can be considered with fraction...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10255641/ |
| _version_ | 1797668040813314048 |
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| author | Juan J. Gude Antonio Di Teodoro Oscar Camacho Pablo Garcia Bringas |
| author_facet | Juan J. Gude Antonio Di Teodoro Oscar Camacho Pablo Garcia Bringas |
| author_sort | Juan J. Gude |
| collection | DOAJ |
| description | This paper presents a new method for identifying dynamical systems to get fractional-reduced-order models based on the process reaction curve. This proposal uses information collected from the process. It can be applied to processes with an S-shaped step response that can be considered with fractional behavior and a fractional order range of <inline-formula> <tex-math notation="LaTeX">$\alpha \in [{0.5, 1.0}]$ </tex-math></inline-formula>. The proposed approach combines obtaining the fractional order of the model using asymptotic properties of the Mittag-Leffler function with time-based parameter estimation by considering two arbitrary points on the process reaction curve. The improvement in terms of accuracy of the identified FFOPDT model is obtained due to a more accurate estimation of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> parameter. This method is characterized by its effectiveness and simplicity of implementation, which are key aspects when applying at an industrial level. Several examples are used to illustrate the effectiveness and simplicity of the proposed method compared to other well-established methods and other approaches based on the process reaction curve. Finally, it is also implemented on microprocessor-based hardware to demonstrate the applicability of the proposed method to identify the fractional model of a thermal process. |
| first_indexed | 2024-03-11T20:22:19Z |
| format | Article |
| id | doaj.art-64191903c66144dbb9e340b2a05f0271 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T20:22:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-64191903c66144dbb9e340b2a05f02712023-10-02T23:01:18ZengIEEEIEEE Access2169-35362023-01-011110321410323110.1109/ACCESS.2023.331723010255641A New Fractional Reduced-Order Model-Inspired System Identification Method for Dynamical SystemsJuan J. Gude0https://orcid.org/0000-0003-4210-2454Antonio Di Teodoro1https://orcid.org/0000-0002-8766-0356Oscar Camacho2https://orcid.org/0000-0001-8827-5938Pablo Garcia Bringas3https://orcid.org/0000-0003-3594-9534Department of Computing, Electronics and Communication Technologies, Faculty of Engineering, University of Deusto, Bilbao, SpainColegio de Ciencias e IngenierÍas ‘‘El Politécnico’’, Universidad San Francisco de Quito (USFQ), Quito, EcuadorColegio de Ciencias e IngenierÍas ‘‘El Politécnico’’, Universidad San Francisco de Quito (USFQ), Quito, EcuadorDepartment of Mechanics, Design and Industrial Management, Faculty of Engineering, University of Deusto, Bilbao, SpainThis paper presents a new method for identifying dynamical systems to get fractional-reduced-order models based on the process reaction curve. This proposal uses information collected from the process. It can be applied to processes with an S-shaped step response that can be considered with fractional behavior and a fractional order range of <inline-formula> <tex-math notation="LaTeX">$\alpha \in [{0.5, 1.0}]$ </tex-math></inline-formula>. The proposed approach combines obtaining the fractional order of the model using asymptotic properties of the Mittag-Leffler function with time-based parameter estimation by considering two arbitrary points on the process reaction curve. The improvement in terms of accuracy of the identified FFOPDT model is obtained due to a more accurate estimation of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> parameter. This method is characterized by its effectiveness and simplicity of implementation, which are key aspects when applying at an industrial level. Several examples are used to illustrate the effectiveness and simplicity of the proposed method compared to other well-established methods and other approaches based on the process reaction curve. Finally, it is also implemented on microprocessor-based hardware to demonstrate the applicability of the proposed method to identify the fractional model of a thermal process.https://ieeexplore.ieee.org/document/10255641/Fractional-order systemsprocess identificationfractional first-order plus dead-time model |
| spellingShingle | Juan J. Gude Antonio Di Teodoro Oscar Camacho Pablo Garcia Bringas A New Fractional Reduced-Order Model-Inspired System Identification Method for Dynamical Systems IEEE Access Fractional-order systems process identification fractional first-order plus dead-time model |
| title | A New Fractional Reduced-Order Model-Inspired System Identification Method for Dynamical Systems |
| title_full | A New Fractional Reduced-Order Model-Inspired System Identification Method for Dynamical Systems |
| title_fullStr | A New Fractional Reduced-Order Model-Inspired System Identification Method for Dynamical Systems |
| title_full_unstemmed | A New Fractional Reduced-Order Model-Inspired System Identification Method for Dynamical Systems |
| title_short | A New Fractional Reduced-Order Model-Inspired System Identification Method for Dynamical Systems |
| title_sort | new fractional reduced order model inspired system identification method for dynamical systems |
| topic | Fractional-order systems process identification fractional first-order plus dead-time model |
| url | https://ieeexplore.ieee.org/document/10255641/ |
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