Nonparametric Predictive Current Control for PMSM

Xin Yuan, Shuo Zhang, Chengning Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)

Abstract

Conventional finite-control set model predictive current control predicts the motor behavior for every switching state and selects the voltage vector that can minimize the cost functions as an optimum voltage vector. To get rid of the model parameter dependency, a finite-control set nonparametric predictive current control (NPCC) was proposed before, which can predict future current behavior using the measured data instead of initial model parameters. However, one of the main issues that are limiting the current and torque ripple performance is a stagnant current update mechanism. To overcome this issue, this article proposes a novel current update mechanism that based on two most recent current variations to reconstruct the permanent magnet synchronous machine model. The proposed nonparametric predictive current control based on the current update mechanism can effectively reduce the torque ripple and enhance current performance in contrast to NPCC. Experiment results are demonstrated to verify the effectiveness of the proposed method under different operating conditions.

Original languageEnglish
Article number8974428
Pages (from-to)9332-9341
Number of pages10
JournalIEEE Transactions on Power Electronics
Volume35
Issue number9
DOIs
Publication statusPublished - 1 Sept 2020
Externally publishedYes

Keywords

  • Finite-control set model predictive current control (MPCC)
  • Nonparametric predictive current control (NPCC)
  • Permanent-magnet synchronous machine

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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