Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor
A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output traj...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kamesh, Reddi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
Single rate and multirate cases Orthonormal polynomial approximations Chemical engineering, Process control, Parameterized Data-driven fuzzy (PDDF) modeling |
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| Umfang: |
13 |
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| Übergeordnetes Werk: |
Enthalten in: Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal - 2012, the science and engineering of measurement and automation, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:64 ; year:2016 ; pages:418-430 ; extent:13 |
| Links: |
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| DOI / URN: |
10.1016/j.isatra.2016.05.016 |
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ELV024862150 |
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| 520 | |a A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. | ||
| 520 | |a A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. | ||
| 650 | 7 | |a Single rate and multirate cases |2 Elsevier | |
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10.1016/j.isatra.2016.05.016 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV024862150 (ELSEVIER)S0019-0578(16)30108-2 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Kamesh, Reddi verfasserin aut Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor 2016transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Single rate and multirate cases Elsevier Optimal control Elsevier Orthonormal polynomial approximations Elsevier Chemical engineering, Process control, Parameterized Data-driven fuzzy (PDDF) modeling Elsevier Rani, K. Yamuna oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:64 year:2016 pages:418-430 extent:13 https://doi.org/10.1016/j.isatra.2016.05.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 64 2016 418-430 13 |
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10.1016/j.isatra.2016.05.016 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV024862150 (ELSEVIER)S0019-0578(16)30108-2 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Kamesh, Reddi verfasserin aut Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor 2016transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Single rate and multirate cases Elsevier Optimal control Elsevier Orthonormal polynomial approximations Elsevier Chemical engineering, Process control, Parameterized Data-driven fuzzy (PDDF) modeling Elsevier Rani, K. Yamuna oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:64 year:2016 pages:418-430 extent:13 https://doi.org/10.1016/j.isatra.2016.05.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 64 2016 418-430 13 |
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10.1016/j.isatra.2016.05.016 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV024862150 (ELSEVIER)S0019-0578(16)30108-2 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Kamesh, Reddi verfasserin aut Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor 2016transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Single rate and multirate cases Elsevier Optimal control Elsevier Orthonormal polynomial approximations Elsevier Chemical engineering, Process control, Parameterized Data-driven fuzzy (PDDF) modeling Elsevier Rani, K. Yamuna oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:64 year:2016 pages:418-430 extent:13 https://doi.org/10.1016/j.isatra.2016.05.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 64 2016 418-430 13 |
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10.1016/j.isatra.2016.05.016 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV024862150 (ELSEVIER)S0019-0578(16)30108-2 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Kamesh, Reddi verfasserin aut Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor 2016transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Single rate and multirate cases Elsevier Optimal control Elsevier Orthonormal polynomial approximations Elsevier Chemical engineering, Process control, Parameterized Data-driven fuzzy (PDDF) modeling Elsevier Rani, K. Yamuna oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:64 year:2016 pages:418-430 extent:13 https://doi.org/10.1016/j.isatra.2016.05.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 64 2016 418-430 13 |
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10.1016/j.isatra.2016.05.016 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001287.pica (DE-627)ELV024862150 (ELSEVIER)S0019-0578(16)30108-2 DE-627 ger DE-627 rakwb eng 540 VZ 660 VZ 540 VZ 35.00 bkl Kamesh, Reddi verfasserin aut Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor 2016transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. Single rate and multirate cases Elsevier Optimal control Elsevier Orthonormal polynomial approximations Elsevier Chemical engineering, Process control, Parameterized Data-driven fuzzy (PDDF) modeling Elsevier Rani, K. Yamuna oth Enthalten in Elsevier Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal 2012 the science and engineering of measurement and automation Amsterdam [u.a.] (DE-627)ELV011067004 volume:64 year:2016 pages:418-430 extent:13 https://doi.org/10.1016/j.isatra.2016.05.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_22 GBV_ILN_40 GBV_ILN_105 35.00 Chemie: Allgemeines VZ AR 64 2016 418-430 13 |
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Enthalten in Selective extraction, structural characterisation and antifungal activity assessment of napins from an industrial rapeseed meal Amsterdam [u.a.] volume:64 year:2016 pages:418-430 extent:13 |
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Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor |
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A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. |
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A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. |
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A parameterized data-driven fuzzy (PDDF) model structure is proposed for semi-batch processes, and its application for optimal control is illustrated. The orthonormally parameterized input trajectories, initial states and process parameters are the inputs to the model, which predicts the output trajectories in terms of Fourier coefficients. Fuzzy rules are formulated based on the signs of a linear data-driven model, while the defuzzification step incorporates a linear regression model to shift the domain from input to output domain. The fuzzy model is employed to formulate an optimal control problem for single rate as well as multi-rate systems. Simulation study on a multivariable semi-batch reactor system reveals that the proposed PDDF modeling approach is capable of capturing the nonlinear and time-varying behavior inherent in the semi-batch system fairly accurately, and the results of operating trajectory optimization using the proposed model are found to be comparable to the results obtained using the exact first principles model, and are also found to be comparable to or better than parameterized data-driven artificial neural network model based optimization results. |
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