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Model Predictive Current Control Using Single Layer Neural Network for PMSM Drives
  • Al-Kaf, Hasan Ali Gamal ;
  • Hakami, Samer Saleh ;
  • Halabi, Laith M. ;
  • Lee, Kyo Beum
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Publication Year
2022-01-01
Journal
2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022
Keyword
model predictive current controlPMSMsingle layer neural networktwo-level inverter
Mesh Keyword
Computational timeModel predictiveModel predictive current controlModel-predictive controlNeural-networksPermanent Magnet Synchronous MotorPredictive current controlSingle layerSingle layer neural networkTwo-level inverters
All Science Classification Codes (ASJC)
Electrical and Electronic EngineeringMechanical EngineeringSafety, Risk, Reliability and QualityEnergy Engineering and Power TechnologyRenewable Energy, Sustainability and the EnvironmentControl and Optimization
Abstract
Model predictive control (MPC) is regarded as a significant modern control for the current control of permanent magnet synchronous motor (PMSM). However, the computation burden of MPC imposes its advantage to be implemented in sophisticated converter topologies and multistep prediction horizons. Multilayer neural network with MPC (MLNN-MPC) is increasingly used in different converters to overcome the drawback of high computational time. However, it has a higher computational time compared to a single-layer neural network (SLNN). In addition, many parameters need to be optimized such as initial weights, number of iterations, and neurons. In this paper, a SLNN with MPC is proposed to predict the current of PMSM. The proposed SLNN-MPC is trained using the Levenberg Marquardt algorithm. Meanwhile, it shows better performance than MLNN-MPC with lower computational time by optimizing only one parameter. Furthermore, the simulation results are shown to verify the effectiveness of the proposed method.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36800
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85144022690&origin=inward
DOI
https://doi.org/10.1109/ecce50734.2022.9947720
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9947299
Type
Conference
Funding
ACKNOWLEDGMENT The This work was supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry & Energy(MOTIE) of the Republic of Korea (No. 20206910100160).
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