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Model Predictive Control for Induction Motor Drives Fed by a Matrix Converter

매트릭스 컨버터로 구동되는 유도전동기의 직접토크제어를 위한 모델예측제어 기반의 SVM 기법

  • Choi, Woo Jin (Dept. of Electrical and Computer Engineering, Ajou University) ;
  • Lee, Eunsil (Dept. of Electrical and Computer Engineering, Ajou University) ;
  • Song, Joong-Ho (Dept. of Electrical and Information Engineering, Seoul National University of Sci. & Tech.) ;
  • Lee, Young-Il (Dept. of Electrical and Information Engineering, Seoul National University of Sci. & Tech.) ;
  • Lee, Kyo-Beum (Dept. of Electrical and Computer Engineering, Ajou University)
  • 최우진 (아주대학교 전자공학과) ;
  • 이은실 (아주대학교 전자공학과) ;
  • 송중호 (서울과학기술대학교 전기정보공학과) ;
  • 이영일 (서울과학기술대학교 전기정보공학과) ;
  • 이교범 (아주대학교 전자공학과)
  • Received : 2013.11.08
  • Accepted : 2014.07.03
  • Published : 2014.09.01

Abstract

This paper proposes a MPC (Model Predictive Control) method for the torque and flux controls of induction motor. The proposed MPC method selects the optimized voltage vector for the matrix converter control using the predictive modeling equation of the induction motor and cost function. Hence, the reference voltage vector that minimizes the cost function of the torque and flux error within the control period is selected and applied to the actual system. As a result, it is possible to perform the torque and flux control of induction motor using only the MPC controller without a PI (Proportional-Integral) or hysteresis controller. Even though the proposed control algorithm is more complicated and has lots of computations compared with the conventional MPC, it can perform torque ripple reduction by synthesizing voltage vectors of various magnitude. This feature provides the reduction of amount of calculations and the improvement of the control performance through the adjustment of the number of the unit vectors n. The proposed control method is validated through the PSIM simulation.

Keywords

References

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Cited by

  1. An Improved Predictive Control of an Induction Machine fed by a Matrix Converter for Torque Ripple Reduction vol.21, pp.7, 2015, https://doi.org/10.5302/J.ICROS.2015.14.0131