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Article

Improvement of Model Predictive Current Control Sensing Strategy for a Developed Small Flux-Switching Permanent Magnet Motor

1
Department of Mechanical and Electro-Mechanical Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
2
Institute of Medical Science and Technology, National Sun Yat-sen University, Kaohsiung 80424, Taiwan
3
Office of Research and Development, Naroller Electronics Co., Ltd., Taoyuan City 33393, Taiwan
*
Authors to whom correspondence should be addressed.
These authors contributed equally.
Sensors 2020, 20(11), 3177; https://doi.org/10.3390/s20113177
Received: 23 April 2020 / Revised: 28 May 2020 / Accepted: 1 June 2020 / Published: 3 June 2020
(This article belongs to the Section Physical Sensors)
This paper presents an improved control system for a small flux-switching permanent magnet motor (FSPM) to enhance its performance and torque sensing. The analytical magnetic circuit design was used to determine the related motor parameters, such as the air gap flux density, permeance coefficient (Pc), torque, winding turns, pole number, width, length, magnet geometry, and the current density of FSPM. The electromagnetic analysis of this motor was performed by software (ANSYS Maxwell) to optimize the motor performance. In this study, the performance of FSPM was investigated by the uniform design experimentation (UDE). For the control system, the model predictive current control (MPCC) is currently recognized as a high-performance control strategy, due to its quick response and simple principle. This model contained the nonlinear part of the system, to improve the torque ripple of FSPM. A modified MPCC strategy was proposed to improve the distortion of the current waveform and decrease the computational burden. The new modified control architecture was mainly composed of three parts, such as the estimation of electromotive force (EMF), current prediction, and optimal vector selection/vector duration. When the reference voltage vector was obtained, the three-phase duties were easily determined by the principle of space vector modulation (SVM). The results show the different strategy methods between the newly proposed modified MPCC and traditional proportional integral (PI) controller. In the control of FSPM, a modified MPCC strategy was able to achieve a better performance response and decrease the computational burden. At a low speed of 350 rpm, the proposed modified MPCC can achieve a better dynamic response. The nonlinear problem of the startup speed was also effectively resolved. The torque sensing performance of the simulation and the experimental test value were compared. The torque sensing performance of the simulation and the actual test value were also examined. In this study, the optimization focused not only on the motor design and fabrication, but also on an improved motor control strategy and torque sensing, in order to achieve the integrity of the FSPM system. View Full-Text
Keywords: model predictive current control (MPCC); flux-switching permanent magnet motor (FSPM); uniform design method; cogging torque; torque sensing model predictive current control (MPCC); flux-switching permanent magnet motor (FSPM); uniform design method; cogging torque; torque sensing
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MDPI and ACS Style

Pan, C.-T.; Wang, S.-Y.; Chang, C.-C.; Yen, C.-K.; Wu, J.-Y.; Ju, S.-P.; Lee, R.C.-L. Improvement of Model Predictive Current Control Sensing Strategy for a Developed Small Flux-Switching Permanent Magnet Motor. Sensors 2020, 20, 3177. https://doi.org/10.3390/s20113177

AMA Style

Pan C-T, Wang S-Y, Chang C-C, Yen C-K, Wu J-Y, Ju S-P, Lee RC-L. Improvement of Model Predictive Current Control Sensing Strategy for a Developed Small Flux-Switching Permanent Magnet Motor. Sensors. 2020; 20(11):3177. https://doi.org/10.3390/s20113177

Chicago/Turabian Style

Pan, Cheng-Tang, Shao-Yu Wang, Chun-Chieh Chang, Chung-Kun Yen, Jyun-Yi Wu, Shin-Pon Ju, and Roger C.-L. Lee. 2020. "Improvement of Model Predictive Current Control Sensing Strategy for a Developed Small Flux-Switching Permanent Magnet Motor" Sensors 20, no. 11: 3177. https://doi.org/10.3390/s20113177

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