Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA
Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux rip...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gdaim, Soufien [verfasserIn] Mtibaa, Abdellatif [verfasserIn] Mimouni, Mohamed Faouzi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Engineering applications of artificial intelligence - Amsterdam [u.a.] : Elsevier Science, 1988, 121 |
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| Übergeordnetes Werk: |
volume:121 |
| DOI / URN: |
10.1016/j.engappai.2023.105972 |
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| Katalog-ID: |
ELV059917431 |
|---|
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| 245 | 1 | 0 | |a Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA |
| 264 | 1 | |c 2023 | |
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| 520 | |a Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux ripples. In this paper, an approach for designing DTC for IMs that is based on Neural Network Control (NNC) is proposed. In this method, an artificial neural network, which can better manage the state of switches, is used instead of the switching table and two hysteresis controllers. The improvement is achieved by reducing the stator flux ripples and the torque ripples in the IM drive. The suggested architecture, which is developed using the VHDL, is designed using the modular architecture and the principles of the parallel architecture. The Direct Torque Neural Control (DTNC) simulation and experimental results are compared with those of the conventional DTC. Results of the comparison demonstrate how the DTNC reduces torque ripples and stator flux ripples. Results from simulation and experiment are used to verify how effectively the suggested control works. The ZC702 SOC, which is Xilinx Board based on Zynq FPGA, has been selected for experimental implementation. | ||
| 650 | 4 | |a Artificial Neural Network | |
| 650 | 4 | |a FPGA | |
| 650 | 4 | |a Direct Torque Control | |
| 650 | 4 | |a Modular architecture | |
| 650 | 4 | |a Induction machine | |
| 700 | 1 | |a Mtibaa, Abdellatif |e verfasserin |4 aut | |
| 700 | 1 | |a Mimouni, Mohamed Faouzi |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Engineering applications of artificial intelligence |d Amsterdam [u.a.] : Elsevier Science, 1988 |g 121 |h Online-Ressource |w (DE-627)308447832 |w (DE-600)1502275-4 |w (DE-576)094752524 |x 0952-1976 |7 nnns |
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| 936 | b | k | |a 50.23 |j Regelungstechnik |j Steuerungstechnik |q VZ |
| 936 | b | k | |a 54.72 |j Künstliche Intelligenz |q VZ |
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2023 |
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50.23 54.72 |
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2023 |
| allfields |
10.1016/j.engappai.2023.105972 doi (DE-627)ELV059917431 (ELSEVIER)S0952-1976(23)00156-2 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Gdaim, Soufien verfasserin (orcid)0000-0002-6569-0116 aut Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux ripples. In this paper, an approach for designing DTC for IMs that is based on Neural Network Control (NNC) is proposed. In this method, an artificial neural network, which can better manage the state of switches, is used instead of the switching table and two hysteresis controllers. The improvement is achieved by reducing the stator flux ripples and the torque ripples in the IM drive. The suggested architecture, which is developed using the VHDL, is designed using the modular architecture and the principles of the parallel architecture. The Direct Torque Neural Control (DTNC) simulation and experimental results are compared with those of the conventional DTC. Results of the comparison demonstrate how the DTNC reduces torque ripples and stator flux ripples. Results from simulation and experiment are used to verify how effectively the suggested control works. The ZC702 SOC, which is Xilinx Board based on Zynq FPGA, has been selected for experimental implementation. Artificial Neural Network FPGA Direct Torque Control Modular architecture Induction machine Mtibaa, Abdellatif verfasserin aut Mimouni, Mohamed Faouzi verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
| spelling |
10.1016/j.engappai.2023.105972 doi (DE-627)ELV059917431 (ELSEVIER)S0952-1976(23)00156-2 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Gdaim, Soufien verfasserin (orcid)0000-0002-6569-0116 aut Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux ripples. In this paper, an approach for designing DTC for IMs that is based on Neural Network Control (NNC) is proposed. In this method, an artificial neural network, which can better manage the state of switches, is used instead of the switching table and two hysteresis controllers. The improvement is achieved by reducing the stator flux ripples and the torque ripples in the IM drive. The suggested architecture, which is developed using the VHDL, is designed using the modular architecture and the principles of the parallel architecture. The Direct Torque Neural Control (DTNC) simulation and experimental results are compared with those of the conventional DTC. Results of the comparison demonstrate how the DTNC reduces torque ripples and stator flux ripples. Results from simulation and experiment are used to verify how effectively the suggested control works. The ZC702 SOC, which is Xilinx Board based on Zynq FPGA, has been selected for experimental implementation. Artificial Neural Network FPGA Direct Torque Control Modular architecture Induction machine Mtibaa, Abdellatif verfasserin aut Mimouni, Mohamed Faouzi verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
| allfields_unstemmed |
10.1016/j.engappai.2023.105972 doi (DE-627)ELV059917431 (ELSEVIER)S0952-1976(23)00156-2 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Gdaim, Soufien verfasserin (orcid)0000-0002-6569-0116 aut Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux ripples. In this paper, an approach for designing DTC for IMs that is based on Neural Network Control (NNC) is proposed. In this method, an artificial neural network, which can better manage the state of switches, is used instead of the switching table and two hysteresis controllers. The improvement is achieved by reducing the stator flux ripples and the torque ripples in the IM drive. The suggested architecture, which is developed using the VHDL, is designed using the modular architecture and the principles of the parallel architecture. The Direct Torque Neural Control (DTNC) simulation and experimental results are compared with those of the conventional DTC. Results of the comparison demonstrate how the DTNC reduces torque ripples and stator flux ripples. Results from simulation and experiment are used to verify how effectively the suggested control works. The ZC702 SOC, which is Xilinx Board based on Zynq FPGA, has been selected for experimental implementation. Artificial Neural Network FPGA Direct Torque Control Modular architecture Induction machine Mtibaa, Abdellatif verfasserin aut Mimouni, Mohamed Faouzi verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
| allfieldsGer |
10.1016/j.engappai.2023.105972 doi (DE-627)ELV059917431 (ELSEVIER)S0952-1976(23)00156-2 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Gdaim, Soufien verfasserin (orcid)0000-0002-6569-0116 aut Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux ripples. In this paper, an approach for designing DTC for IMs that is based on Neural Network Control (NNC) is proposed. In this method, an artificial neural network, which can better manage the state of switches, is used instead of the switching table and two hysteresis controllers. The improvement is achieved by reducing the stator flux ripples and the torque ripples in the IM drive. The suggested architecture, which is developed using the VHDL, is designed using the modular architecture and the principles of the parallel architecture. The Direct Torque Neural Control (DTNC) simulation and experimental results are compared with those of the conventional DTC. Results of the comparison demonstrate how the DTNC reduces torque ripples and stator flux ripples. Results from simulation and experiment are used to verify how effectively the suggested control works. The ZC702 SOC, which is Xilinx Board based on Zynq FPGA, has been selected for experimental implementation. Artificial Neural Network FPGA Direct Torque Control Modular architecture Induction machine Mtibaa, Abdellatif verfasserin aut Mimouni, Mohamed Faouzi verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
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10.1016/j.engappai.2023.105972 doi (DE-627)ELV059917431 (ELSEVIER)S0952-1976(23)00156-2 DE-627 ger DE-627 rda eng 004 VZ 50.23 bkl 54.72 bkl Gdaim, Soufien verfasserin (orcid)0000-0002-6569-0116 aut Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux ripples. In this paper, an approach for designing DTC for IMs that is based on Neural Network Control (NNC) is proposed. In this method, an artificial neural network, which can better manage the state of switches, is used instead of the switching table and two hysteresis controllers. The improvement is achieved by reducing the stator flux ripples and the torque ripples in the IM drive. The suggested architecture, which is developed using the VHDL, is designed using the modular architecture and the principles of the parallel architecture. The Direct Torque Neural Control (DTNC) simulation and experimental results are compared with those of the conventional DTC. Results of the comparison demonstrate how the DTNC reduces torque ripples and stator flux ripples. Results from simulation and experiment are used to verify how effectively the suggested control works. The ZC702 SOC, which is Xilinx Board based on Zynq FPGA, has been selected for experimental implementation. Artificial Neural Network FPGA Direct Torque Control Modular architecture Induction machine Mtibaa, Abdellatif verfasserin aut Mimouni, Mohamed Faouzi verfasserin aut Enthalten in Engineering applications of artificial intelligence Amsterdam [u.a.] : Elsevier Science, 1988 121 Online-Ressource (DE-627)308447832 (DE-600)1502275-4 (DE-576)094752524 0952-1976 nnns volume:121 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 50.23 Regelungstechnik Steuerungstechnik VZ 54.72 Künstliche Intelligenz VZ AR 121 |
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004 VZ 50.23 bkl 54.72 bkl Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA Artificial Neural Network FPGA Direct Torque Control Modular architecture Induction machine |
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ddc 004 bkl 50.23 bkl 54.72 misc Artificial Neural Network misc FPGA misc Direct Torque Control misc Modular architecture misc Induction machine |
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ddc 004 bkl 50.23 bkl 54.72 misc Artificial Neural Network misc FPGA misc Direct Torque Control misc Modular architecture misc Induction machine |
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ddc 004 bkl 50.23 bkl 54.72 misc Artificial Neural Network misc FPGA misc Direct Torque Control misc Modular architecture misc Induction machine |
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Elektronische Aufsätze Aufsätze Elektronische Ressource |
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Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA |
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Gdaim, Soufien |
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Engineering applications of artificial intelligence |
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Gdaim, Soufien Mtibaa, Abdellatif Mimouni, Mohamed Faouzi |
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artificial neural network-based dtc of an induction machine with experimental implementation on fpga |
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Artificial neural network-based DTC of an induction machine with experimental implementation on FPGA |
| abstract |
Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux ripples. In this paper, an approach for designing DTC for IMs that is based on Neural Network Control (NNC) is proposed. In this method, an artificial neural network, which can better manage the state of switches, is used instead of the switching table and two hysteresis controllers. The improvement is achieved by reducing the stator flux ripples and the torque ripples in the IM drive. The suggested architecture, which is developed using the VHDL, is designed using the modular architecture and the principles of the parallel architecture. The Direct Torque Neural Control (DTNC) simulation and experimental results are compared with those of the conventional DTC. Results of the comparison demonstrate how the DTNC reduces torque ripples and stator flux ripples. Results from simulation and experiment are used to verify how effectively the suggested control works. The ZC702 SOC, which is Xilinx Board based on Zynq FPGA, has been selected for experimental implementation. |
| abstractGer |
Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux ripples. In this paper, an approach for designing DTC for IMs that is based on Neural Network Control (NNC) is proposed. In this method, an artificial neural network, which can better manage the state of switches, is used instead of the switching table and two hysteresis controllers. The improvement is achieved by reducing the stator flux ripples and the torque ripples in the IM drive. The suggested architecture, which is developed using the VHDL, is designed using the modular architecture and the principles of the parallel architecture. The Direct Torque Neural Control (DTNC) simulation and experimental results are compared with those of the conventional DTC. Results of the comparison demonstrate how the DTNC reduces torque ripples and stator flux ripples. Results from simulation and experiment are used to verify how effectively the suggested control works. The ZC702 SOC, which is Xilinx Board based on Zynq FPGA, has been selected for experimental implementation. |
| abstract_unstemmed |
Direct Torque Control (DTC) of Induction Machine (IM) has received increasing attention due to its high performance and low dependence on machine parameters. Recently, intelligent approaches have been proposed to improve the DTC performance, in particular the reduction in the torque and the flux ripples. In this paper, an approach for designing DTC for IMs that is based on Neural Network Control (NNC) is proposed. In this method, an artificial neural network, which can better manage the state of switches, is used instead of the switching table and two hysteresis controllers. The improvement is achieved by reducing the stator flux ripples and the torque ripples in the IM drive. The suggested architecture, which is developed using the VHDL, is designed using the modular architecture and the principles of the parallel architecture. The Direct Torque Neural Control (DTNC) simulation and experimental results are compared with those of the conventional DTC. Results of the comparison demonstrate how the DTNC reduces torque ripples and stator flux ripples. Results from simulation and experiment are used to verify how effectively the suggested control works. The ZC702 SOC, which is Xilinx Board based on Zynq FPGA, has been selected for experimental implementation. |
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