Artificial Neural Network-Based Deadbeat Predictive Current Control with Dead-Time Compensation for PMSMs
In the velocity control of Permanent Magnet Synchronous Motors (PMSMs), Deadbeat Predictive Current Controllers (DPCCs) are renowned for their excellent dynamic performance and constant switching frequency. However, achieving precise velocity regulation remains challenging due...
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| Format: | Article |
| Language: | English |
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Institute of Technology and Education Galileo da Amazônia
2025-02-01
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| Series: | ITEGAM-JETIA |
| Online Access: | https://itegam-jetia.org/journal/index.php/jetia/article/view/1456 |
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| author | amira amira Slimani Amor Bourek Abdelkarim Ammar Khoudir Kakouche Wassila Hattab Marah Bacha |
| author_facet | amira amira Slimani Amor Bourek Abdelkarim Ammar Khoudir Kakouche Wassila Hattab Marah Bacha |
| author_sort | amira amira Slimani |
| collection | DOAJ |
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In the velocity control of Permanent Magnet Synchronous Motors (PMSMs), Deadbeat Predictive Current Controllers (DPCCs) are renowned for their excellent dynamic performance and constant switching frequency. However, achieving precise velocity regulation remains challenging due to the nonlinearities introduced by two-level voltage source inverter (2L-VSI). Specifically, the dead time inherent in 2L-VSI results in voltage distortion, which generates parasitic harmonics in the system. These harmonics degrade control accuracy, cause a current ripple, and can lead to performance degradation or even system instability, compromising reliable operation. This article proposes an innovative solution: Artificial Neural Network-Based Deadbeat Predictive Current Control (ANN-DPCC) integrated with dead-time compensation to address these issues. This approach effectively suppresses the current ripple and significantly reduces total harmonic distortion (THD). Simulation results validate that ANN-DPCC with dead-time compensation outperforms traditional DPCC by improving response times, enhancing steady-state accuracy, and minimizing current distortions. This novel strategy significantly advances PMSM control, offering precise velocity regulation, improved reliability, and superior system performance for demanding applications
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| format | Article |
| id | doaj-art-376c82636b714adda6d8bf8bc8529325 |
| institution | DOAJ |
| issn | 2447-0228 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Institute of Technology and Education Galileo da Amazônia |
| record_format | Article |
| series | ITEGAM-JETIA |
| spelling | doaj-art-376c82636b714adda6d8bf8bc85293252025-08-20T03:00:27ZengInstitute of Technology and Education Galileo da AmazôniaITEGAM-JETIA2447-02282025-02-01115110.5935/jetia.v11i51.1456Artificial Neural Network-Based Deadbeat Predictive Current Control with Dead-Time Compensation for PMSMsamira amira Slimani0Amor Bourek1Abdelkarim Ammar2Khoudir Kakouche3Wassila Hattab4Marah Bacha5dept. Electrical Engineering-LGEB Lab, Biskra University Biskra, Algeriadept. Electrical Engineering-LGEB Lab, Biskra University Biskra, AlgeriaInstitute for Electrical and Electronics Engineering -LSS Lab Boumerdes University Boumerdes, AlgeriaUniversité de Bejaia, Faculté de Technologie, Laboratoire de Technologie Industrielle et de l’Information, Bejaia 06000, Algeriadept. Electrical Engineering-LGEB Lab, Biskra University Biskra, Algeriadept. Electrical Engineering-LGEB Lab, Biskra University Biskra, Algeria In the velocity control of Permanent Magnet Synchronous Motors (PMSMs), Deadbeat Predictive Current Controllers (DPCCs) are renowned for their excellent dynamic performance and constant switching frequency. However, achieving precise velocity regulation remains challenging due to the nonlinearities introduced by two-level voltage source inverter (2L-VSI). Specifically, the dead time inherent in 2L-VSI results in voltage distortion, which generates parasitic harmonics in the system. These harmonics degrade control accuracy, cause a current ripple, and can lead to performance degradation or even system instability, compromising reliable operation. This article proposes an innovative solution: Artificial Neural Network-Based Deadbeat Predictive Current Control (ANN-DPCC) integrated with dead-time compensation to address these issues. This approach effectively suppresses the current ripple and significantly reduces total harmonic distortion (THD). Simulation results validate that ANN-DPCC with dead-time compensation outperforms traditional DPCC by improving response times, enhancing steady-state accuracy, and minimizing current distortions. This novel strategy significantly advances PMSM control, offering precise velocity regulation, improved reliability, and superior system performance for demanding applications https://itegam-jetia.org/journal/index.php/jetia/article/view/1456 |
| spellingShingle | amira amira Slimani Amor Bourek Abdelkarim Ammar Khoudir Kakouche Wassila Hattab Marah Bacha Artificial Neural Network-Based Deadbeat Predictive Current Control with Dead-Time Compensation for PMSMs ITEGAM-JETIA |
| title | Artificial Neural Network-Based Deadbeat Predictive Current Control with Dead-Time Compensation for PMSMs |
| title_full | Artificial Neural Network-Based Deadbeat Predictive Current Control with Dead-Time Compensation for PMSMs |
| title_fullStr | Artificial Neural Network-Based Deadbeat Predictive Current Control with Dead-Time Compensation for PMSMs |
| title_full_unstemmed | Artificial Neural Network-Based Deadbeat Predictive Current Control with Dead-Time Compensation for PMSMs |
| title_short | Artificial Neural Network-Based Deadbeat Predictive Current Control with Dead-Time Compensation for PMSMs |
| title_sort | artificial neural network based deadbeat predictive current control with dead time compensation for pmsms |
| url | https://itegam-jetia.org/journal/index.php/jetia/article/view/1456 |
| work_keys_str_mv | AT amiraamiraslimani artificialneuralnetworkbaseddeadbeatpredictivecurrentcontrolwithdeadtimecompensationforpmsms AT amorbourek artificialneuralnetworkbaseddeadbeatpredictivecurrentcontrolwithdeadtimecompensationforpmsms AT abdelkarimammar artificialneuralnetworkbaseddeadbeatpredictivecurrentcontrolwithdeadtimecompensationforpmsms AT khoudirkakouche artificialneuralnetworkbaseddeadbeatpredictivecurrentcontrolwithdeadtimecompensationforpmsms AT wassilahattab artificialneuralnetworkbaseddeadbeatpredictivecurrentcontrolwithdeadtimecompensationforpmsms AT marahbacha artificialneuralnetworkbaseddeadbeatpredictivecurrentcontrolwithdeadtimecompensationforpmsms |