Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor
The goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing an intelligent computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order to adjust the nonlinear and time-var...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10131686/ |
| _version_ | 1797808495698903040 |
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| author | Faa-Jeng Lin Ming-Shi Huang Chung-Yu Hung Yu-Chen Chien |
| author_facet | Faa-Jeng Lin Ming-Shi Huang Chung-Yu Hung Yu-Chen Chien |
| author_sort | Faa-Jeng Lin |
| collection | DOAJ |
| description | The goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing an intelligent computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order to adjust the nonlinear and time-varying control specifications of the motor. The team first proposes an ANSYS Maxwell-2D dynamic model that contains a maximum torque per ampere (MTPA) control PMASynRM drive. A lookup table (LUT) is composed of the finite element analysis (FEA) results, which bring about the current angle of command within the MTPA. Subsequently, the team designs a computed torque control (CTC) system to control the speed reference command. Creating a working CTC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the PMASynRM drive system, is not available beforehand. Thus, this study suggests that a recurrent Legendre fuzzy neural network (RLFNN) can act as a close substitute for the CTC to resolve its existing complications. Furthermore, the team modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the RLFNN. Asymptotical stability is assured by using the Lyapunov stability method, which generates the RLFNN’s online learning algorithms. This study concludes that certain experimental results verify the effective and robust qualities of the suggested ICTCRLFNN controlled PMASynRM drive. |
| first_indexed | 2024-03-13T06:39:22Z |
| format | Article |
| id | doaj.art-afa224c35c6d4001a17e46aef26922fe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T06:39:22Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-afa224c35c6d4001a17e46aef26922fe2023-06-08T23:00:30ZengIEEEIEEE Access2169-35362023-01-0111540175402810.1109/ACCESS.2023.327927510131686Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance MotorFaa-Jeng Lin0https://orcid.org/0000-0003-4717-1993Ming-Shi Huang1https://orcid.org/0000-0002-5303-0065Chung-Yu Hung2Yu-Chen Chien3Department of Electrical Engineering, National Central University, Taoyuan City, TaiwanDepartment of Electrical Engineering, National Taipei University of Technology, Taipei City, TaiwanDepartment of Electrical Engineering, National Central University, Taoyuan City, TaiwanDepartment of Electrical Engineering, National Central University, Taoyuan City, TaiwanThe goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing an intelligent computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order to adjust the nonlinear and time-varying control specifications of the motor. The team first proposes an ANSYS Maxwell-2D dynamic model that contains a maximum torque per ampere (MTPA) control PMASynRM drive. A lookup table (LUT) is composed of the finite element analysis (FEA) results, which bring about the current angle of command within the MTPA. Subsequently, the team designs a computed torque control (CTC) system to control the speed reference command. Creating a working CTC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the PMASynRM drive system, is not available beforehand. Thus, this study suggests that a recurrent Legendre fuzzy neural network (RLFNN) can act as a close substitute for the CTC to resolve its existing complications. Furthermore, the team modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the RLFNN. Asymptotical stability is assured by using the Lyapunov stability method, which generates the RLFNN’s online learning algorithms. This study concludes that certain experimental results verify the effective and robust qualities of the suggested ICTCRLFNN controlled PMASynRM drive.https://ieeexplore.ieee.org/document/10131686/Permanent-magnet assisted synchronous reluctance motor (PMASynRM)computed torque control (CTC)intelligent computed torque control using recurrent Legendre fuzzy neural network (ICTCRLFNN)maximum torque per ampere (MTPA) |
| spellingShingle | Faa-Jeng Lin Ming-Shi Huang Chung-Yu Hung Yu-Chen Chien Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor IEEE Access Permanent-magnet assisted synchronous reluctance motor (PMASynRM) computed torque control (CTC) intelligent computed torque control using recurrent Legendre fuzzy neural network (ICTCRLFNN) maximum torque per ampere (MTPA) |
| title | Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor |
| title_full | Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor |
| title_fullStr | Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor |
| title_full_unstemmed | Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor |
| title_short | Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor |
| title_sort | intelligent computed torque control with recurrent legendre fuzzy neural network for permanent magnet assisted synchronous reluctance motor |
| topic | Permanent-magnet assisted synchronous reluctance motor (PMASynRM) computed torque control (CTC) intelligent computed torque control using recurrent Legendre fuzzy neural network (ICTCRLFNN) maximum torque per ampere (MTPA) |
| url | https://ieeexplore.ieee.org/document/10131686/ |
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