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Long-Prediction-Horizon FCS-MPC of Six-Phase Permanent Magnet Synchronous Motor Based on K-Best Sphere Decoding

  • Mengdi Wang*
  • , Yixiao Luo
  • , Kai Yang
  • , Jincheng Yu
  • *Corresponding author for this work
  • Huazhong University of Science and Technology
  • Harbin Institute of Technology Shenzhen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Extending the prediction horizon can improve the control performance of the finite control set model predictive control (FCS-MPC) for motor drives. Nevertheless, the computational burden is inevitably increased. Meanwhile, the number of the prediction vectors for the six-phase motor is increased exponentially. To overcome this problem, this paper presents a K-best sphere decoding algorithm. The key is that the priority of the search strategy in the conventional sphere decoding is modified as breadth first. Simulation results are provided to prove the feasibility of the proposed method.

Original languageEnglish
Title of host publication2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3890-3895
Number of pages6
ISBN (Electronic)9798350317589
DOIs
StatePublished - 2023
Externally publishedYes
Event26th International Conference on Electrical Machines and Systems, ICEMS 2023 - Zhuhai, China
Duration: 5 Nov 20238 Nov 2023

Publication series

Name2023 26th International Conference on Electrical Machines and Systems, ICEMS 2023

Conference

Conference26th International Conference on Electrical Machines and Systems, ICEMS 2023
Country/TerritoryChina
CityZhuhai
Period5/11/238/11/23

Keywords

  • model predictive control
  • sphere decoding

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