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Low Complexity MPC-DSVPWM for Current Control of PMSM Using Neural Network Approachoa mark
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Publication Year
2022-01-01
Journal
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Access, Vol.10, pp.132596-132607
Keyword
Artificial neural network (ANN)discrete space vector pulse width modulation (DSVPWM)execution timemodel predictive control (MPC)permanent magnet synchronous motor (PMSM)
Mesh Keyword
Artificial neural networkBiological neural networksDiscrete space vector pulse width modulationDiscrete spacesExecution timeInverterModel predictive controlModel-predictive controlPermanent magnet synchronoi motorPermanent Magnet Synchronous MotorPredictive controlSpace vector pulse width modulation
All Science Classification Codes (ASJC)
Computer Science (all)Materials Science (all)Engineering (all)Electrical and Electronic Engineering
Abstract
Multilayer neural network-based model predictive control (MLNN-MPC) has received a lot of attention in different power electronic applications. However, the computational burden often imposes limitations in low-order DSPs especially if a large number of voltage vectors (VVs) are used. The execution time of MLNN-MPC in low-order DSPs is affected heavily by the number of input, output, neurons in the hidden layer, and the type of activation function. Furthermore, MLNN contains many parameters that needed to be optimized, such as initial weights, number of iterations, and number of neurons. Therefore, in this study, a creative single-layer neural network-based model predictive control with discrete space vector PWM (SLNN-MPC-DSVPWM) is proposed to overcome these limitations. The main advantages of the proposed method include easy implementation on low-order DSPs, better performance compared with MLNN-MPC, allowing the use of a large number of VVs, and no initialization of lookup tables for all VVs. The proposed SLNN is trained using the Levenberg-Marquardt algorithm and results in an execution time of only 8μs compared with the complexity of the conventional MPC-DSVPWM and recent MLNN-MPC methods. The SLNN-MPC-DSVPWM is validated by both simulation and experimental results for permanent magnet synchronous motors.
ISSN
2169-3536
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/33198
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85146223136&origin=inward
DOI
https://doi.org/2-s2.0-85146223136
Journal URL
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639
Type
Article
Funding
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea under Grant 20206910100160 and Grant 20225500000110.
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Lee, Kyo-Beum이교범
Department of Electrical and Computer Engineering
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