Fault tolerant control strategy based on model predictive control and unscented Kalman filter for permanent magnet synchronous motor

Ahmed Aboelhassan, Waseem El Sayed, Ahmed Hebala, Michael Galea, Serhiy Bozhko

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

Abstract

Permanent Magnet Synchronous Motors (PMSMs) are now extensively used in many critical applications. There is an increasing need for the motor and control system to have fault tolerant capabilities. This paper presents a fault tolerant control strategy to operate the PMSM during inter-turn fault conditions. The proposed technique combines the Model Predictive Control (MPC) for the speed and current control loops, and an almost error-free Unscented Kalman Filter (UKF) to estimate the PMSM inter-turn fault ratio. The PMSM state-space model for healthy and faulty conditions will be presented. Also, the equations and the remedial action of the MPC and UKF are provided in detail. The proposed algorithm is applied to PMSM model as a case study with a range of simulation analysis and discussion of results.

Original languageEnglish
Title of host publicationProceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages153-159
Number of pages7
ISBN (Electronic)9781665422482
DOIs
Publication statusPublished - 1 Aug 2021
Event16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021 - Chengdu, China
Duration: 1 Aug 20214 Aug 2021

Publication series

NameProceedings of the 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021

Conference

Conference16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021
Country/TerritoryChina
CityChengdu
Period1/08/214/08/21

Keywords

  • Model predictive control
  • PMSM
  • fault diagnosis
  • fault tolerant
  • unscented Kalman filter

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Instrumentation
  • Computer Vision and Pattern Recognition
  • Energy Engineering and Power Technology

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