Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors
Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based po...
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MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/19/4067 |
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| author | Su-Dan Huang Zhixiang Lin Guang-Zhong Cao Ningpeng Liu Hongda Mou Junqi Xu |
| author_facet | Su-Dan Huang Zhixiang Lin Guang-Zhong Cao Ningpeng Liu Hongda Mou Junqi Xu |
| author_sort | Su-Dan Huang |
| collection | DOAJ |
| description | Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position control parameter optimization method of PSRMs is proposed in this paper. First, to improve the accuracy of the motor dynamics model, a Hammerstein–Wiener model based on the BP neural network input–output nonlinear module is established by combining the linear model and nonlinear model structures so that the nonlinear and linear characteristics of the system are characterized simultaneously. Then, a position control parameter optimization system of PSRMs is developed using the established Hammerstein–Wiener model. In addition, with a self-designed simulated annealing adaptive particle swarm optimization algorithm (SAAPSO), the position control parameter optimization system is performed offline iteratively to obtain the optimal position control parameters. Simulations and experiments are carried out and the corresponding results show that the optimal position control parameters obtained by the proposed method can be directly applied in the actual control system of PSRMs and the control performance is improved effectively using the obtained optimal control parameters. |
| first_indexed | 2024-03-10T21:40:35Z |
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| id | doaj.art-3c90d3c336234c54b21f9b67ee385f17 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T21:40:35Z |
| publishDate | 2023-09-01 |
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| series | Mathematics |
| spelling | doaj.art-3c90d3c336234c54b21f9b67ee385f172023-11-19T14:42:55ZengMDPI AGMathematics2227-73902023-09-011119406710.3390/math11194067Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance MotorsSu-Dan Huang0Zhixiang Lin1Guang-Zhong Cao2Ningpeng Liu3Hongda Mou4Junqi Xu5Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaGuangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaGuangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaGuangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaGuangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, ChinaNational Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, ChinaCurrently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position control parameter optimization method of PSRMs is proposed in this paper. First, to improve the accuracy of the motor dynamics model, a Hammerstein–Wiener model based on the BP neural network input–output nonlinear module is established by combining the linear model and nonlinear model structures so that the nonlinear and linear characteristics of the system are characterized simultaneously. Then, a position control parameter optimization system of PSRMs is developed using the established Hammerstein–Wiener model. In addition, with a self-designed simulated annealing adaptive particle swarm optimization algorithm (SAAPSO), the position control parameter optimization system is performed offline iteratively to obtain the optimal position control parameters. Simulations and experiments are carried out and the corresponding results show that the optimal position control parameters obtained by the proposed method can be directly applied in the actual control system of PSRMs and the control performance is improved effectively using the obtained optimal control parameters.https://www.mdpi.com/2227-7390/11/19/4067Hammerstein–Wiener modelnonlinear dynamic modelplanar switched reluctance motorparticle swarm optimization algorithmsimulated annealing algorithm |
| spellingShingle | Su-Dan Huang Zhixiang Lin Guang-Zhong Cao Ningpeng Liu Hongda Mou Junqi Xu Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors Mathematics Hammerstein–Wiener model nonlinear dynamic model planar switched reluctance motor particle swarm optimization algorithm simulated annealing algorithm |
| title | Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors |
| title_full | Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors |
| title_fullStr | Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors |
| title_full_unstemmed | Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors |
| title_short | Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors |
| title_sort | nonlinear dynamic model based position control parameter optimization method of planar switched reluctance motors |
| topic | Hammerstein–Wiener model nonlinear dynamic model planar switched reluctance motor particle swarm optimization algorithm simulated annealing algorithm |
| url | https://www.mdpi.com/2227-7390/11/19/4067 |
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