Enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling
Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or freque...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Yilmaz, Serdar [verfasserIn] Sedef, Herman [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of the Brazilian Society of Mechanical Sciences and Engineering - Springer Berlin Heidelberg, 2003, 46(2024), 7 vom: 27. Mai |
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| Übergeordnetes Werk: |
volume:46 ; year:2024 ; number:7 ; day:27 ; month:05 |
| Links: |
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| DOI / URN: |
10.1007/s40430-024-04979-8 |
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| Katalog-ID: |
SPR056008856 |
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| 520 | |a Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure. | ||
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10.1007/s40430-024-04979-8 doi (DE-627)SPR056008856 (SPR)s40430-024-04979-8-e DE-627 ger DE-627 rakwb eng 600 620 670 VZ 52.00 bkl Yilmaz, Serdar verfasserin (orcid)0000-0002-4316-3737 aut Enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure. Model-based control (dpeaa)DE-He213 Luenberger controller (dpeaa)DE-He213 In vitro (dpeaa)DE-He213 Hemodynamics (dpeaa)DE-He213 Parametric identification (dpeaa)DE-He213 Pulsatile waveform (dpeaa)DE-He213 Sedef, Herman verfasserin aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Springer Berlin Heidelberg, 2003 46(2024), 7 vom: 27. Mai (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:46 year:2024 number:7 day:27 month:05 https://dx.doi.org/10.1007/s40430-024-04979-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 VZ AR 46 2024 7 27 05 |
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10.1007/s40430-024-04979-8 doi (DE-627)SPR056008856 (SPR)s40430-024-04979-8-e DE-627 ger DE-627 rakwb eng 600 620 670 VZ 52.00 bkl Yilmaz, Serdar verfasserin (orcid)0000-0002-4316-3737 aut Enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure. Model-based control (dpeaa)DE-He213 Luenberger controller (dpeaa)DE-He213 In vitro (dpeaa)DE-He213 Hemodynamics (dpeaa)DE-He213 Parametric identification (dpeaa)DE-He213 Pulsatile waveform (dpeaa)DE-He213 Sedef, Herman verfasserin aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Springer Berlin Heidelberg, 2003 46(2024), 7 vom: 27. Mai (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:46 year:2024 number:7 day:27 month:05 https://dx.doi.org/10.1007/s40430-024-04979-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 VZ AR 46 2024 7 27 05 |
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10.1007/s40430-024-04979-8 doi (DE-627)SPR056008856 (SPR)s40430-024-04979-8-e DE-627 ger DE-627 rakwb eng 600 620 670 VZ 52.00 bkl Yilmaz, Serdar verfasserin (orcid)0000-0002-4316-3737 aut Enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure. Model-based control (dpeaa)DE-He213 Luenberger controller (dpeaa)DE-He213 In vitro (dpeaa)DE-He213 Hemodynamics (dpeaa)DE-He213 Parametric identification (dpeaa)DE-He213 Pulsatile waveform (dpeaa)DE-He213 Sedef, Herman verfasserin aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Springer Berlin Heidelberg, 2003 46(2024), 7 vom: 27. Mai (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:46 year:2024 number:7 day:27 month:05 https://dx.doi.org/10.1007/s40430-024-04979-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 VZ AR 46 2024 7 27 05 |
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10.1007/s40430-024-04979-8 doi (DE-627)SPR056008856 (SPR)s40430-024-04979-8-e DE-627 ger DE-627 rakwb eng 600 620 670 VZ 52.00 bkl Yilmaz, Serdar verfasserin (orcid)0000-0002-4316-3737 aut Enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure. Model-based control (dpeaa)DE-He213 Luenberger controller (dpeaa)DE-He213 In vitro (dpeaa)DE-He213 Hemodynamics (dpeaa)DE-He213 Parametric identification (dpeaa)DE-He213 Pulsatile waveform (dpeaa)DE-He213 Sedef, Herman verfasserin aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Springer Berlin Heidelberg, 2003 46(2024), 7 vom: 27. Mai (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:46 year:2024 number:7 day:27 month:05 https://dx.doi.org/10.1007/s40430-024-04979-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 VZ AR 46 2024 7 27 05 |
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10.1007/s40430-024-04979-8 doi (DE-627)SPR056008856 (SPR)s40430-024-04979-8-e DE-627 ger DE-627 rakwb eng 600 620 670 VZ 52.00 bkl Yilmaz, Serdar verfasserin (orcid)0000-0002-4316-3737 aut Enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure. Model-based control (dpeaa)DE-He213 Luenberger controller (dpeaa)DE-He213 In vitro (dpeaa)DE-He213 Hemodynamics (dpeaa)DE-He213 Parametric identification (dpeaa)DE-He213 Pulsatile waveform (dpeaa)DE-He213 Sedef, Herman verfasserin aut Enthalten in Journal of the Brazilian Society of Mechanical Sciences and Engineering Springer Berlin Heidelberg, 2003 46(2024), 7 vom: 27. Mai (DE-627)387477950 (DE-600)2145288-X 1806-3691 nnns volume:46 year:2024 number:7 day:27 month:05 https://dx.doi.org/10.1007/s40430-024-04979-8 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 52.00 VZ AR 46 2024 7 27 05 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. 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Yilmaz, Serdar |
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enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling |
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Enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling |
| abstract |
Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract This research uses parametric polynomial techniques to determine model-based control parameters of a dynamic fluid control system. The system identification process suitable for this purpose involves creating a mathematical model of a dynamic system using data measured in the time or frequency domain. The approach consists of fitting a polynomial function to the input–output data, with polynomial parameters representing the unknown parameters of the system. The aim is to estimate these parameters in order to control the system effectively. In this study, aortic, femoral, iliac, carotid and coronary artery signals were used in modeling and testing the flow system during parametric model studies, as the system forms the basis for hemodynamic research. Autoregressive model with external input, autoregressive moving average model with external input, Box–Jenkins, output-error and state space model parametric models were used in the modeling process. In the mathematical performance analysis, the transfer function of the most successful parametric model was calculated. AC motor and centrifugal pump were used as flow control devices. The transfer function with the most successful performance in the observer design was used as the Luenberger controller. An innovative closed-loop control system has been obtained using the Luenberger structure. © The Author(s), under exclusive licence to The Brazilian Society of Mechanical Sciences and Engineering 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
Enhancing control of dynamic flow systems through innovator controller design and parametric polynomial modeling |
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https://dx.doi.org/10.1007/s40430-024-04979-8 |
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| score |
7.4018965 |