Eliminating Current Sensors in Simplified Predictive Direct Voltage Control of RSM

Arash Fereidooni, S. Alireza Davari, Cristian Garcia, Jose Rodriguez

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

Abstract

Reluctance synchronous motor (RSM) has attracted the attentions because of its high efficiency. The predictive control, as an advanced control method, has also been used to control this motor. The simplified FCS-MPC (finite control set model predictive control) method has become a successful version of the predictive control because of its simplicity, reduced computation, and elimination of the weighting factor. In this method, to predict the current, it is necessary to have two current sensors to measure the currents. Therefore, the possible failure of the current sensors will stop the process in the control loop. In this paper, the elimination of all current sensors via an observer is proposed for the simplified FCS-MPC method. This issue will increase the reliability of the control method. Also, removing the current sensors reduces the cost of the hardware. The performance of this observer has been evaluated by simulation.

Original languageEnglish
Title of host publication2020 11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728158495
DOIs
Publication statusPublished - Feb 2020
Event11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020 - Tehran, Iran, Islamic Republic of
Duration: 4 Feb 20206 Feb 2020

Publication series

Name2020 11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020

Conference

Conference11th Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2020
CountryIran, Islamic Republic of
CityTehran
Period4/02/206/02/20

Keywords

  • Remove current sensor
  • RSM
  • simplified FCS_MPC

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Automotive Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Control and Optimization

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