Predictive Error Model Based Enhanced Observer for PMSM Deadbeat Control Systems

Dongliang Ke, Fengxiang Wang, Xinhong Yu, S. Alireza Davari, Ralph Kennel

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

To achieve high robustness and performance, this paper proposes an enhanced observer based on a predictive error model for deadbeat predictive current control method (DPCC-PEMO). Firstly, the mathematical model of permanent magnet synchronous motor (PMSM), deadbeat predictive current control (DPCC) method and disturbance analysis are presented. Secondly, to estimate the trend of current variation in advance under non-linear disturbance effect of model mismatch, the predictive error model is designed based on recursive least squares (RLS) algorithm. Furthermore, the enhanced observer combined with the predictive error model (PEMO) is proposed and its stability and high convergence are proved. Finally, utilizing the PEMO, the predictive current model is established and the proposed DPCC method is developed. The experimental results validate the strong robustness and excellent dynamic tracking performance of proposed method in PMSM systems.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Industrial Electronics
DOIs
Publication statusAccepted/In press - 2023
Externally publishedYes

Keywords

  • Current control
  • deadbeat predictive current control (DPCC)
  • Mathematical models
  • observer
  • Observers
  • permanent magnet synchronous motor (PMSM)
  • Predictive control
  • predictive error model
  • Predictive models
  • Robustness
  • Voltage control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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