An Indirect Reference Vector-Based Model Predictive Control for a Three-Phase PMSM Motor

Shuangxia Niu, Yixiao Luo, Weinong Fu, Xiaodong Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)

Abstract

Almost all of the existing reference vector based finite control set-model predictive control (FCS-MPC) methods for electric machines adopt the deadbeat control principle to directly obtain the reference vector. This paper proposes a computationally efficient control approach for a three phase permanent magnet synchronous motor (PMSM) based on indirect reference vector without using the deadbeat control. Instead of calculating a virtual reference vector in the traditional manner using deadbeat control, the reference vector is innovatively determined through two two-level bang-bang comparators. To improve the performance of the machine, the sampling period is subdivided into two equal time intervals and total 20 synthesized voltage vectors are obtained. Nevertheless, there is no need to evaluate all the 20 vectors by excluding the inappropriate vectors in advance using the reference vector based method, thus reducing the computation time. Moreover, with this proposed method, the complicated calculation of reference vector is avoided. Simulation and experimental results are presented to prove the effectiveness of the proposed method.

Original languageEnglish
Article number8967065
Pages (from-to)29435-29445
Number of pages11
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Direct torque control
  • discrete space vector
  • model predictive control
  • PMSM motor

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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