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Finite-Set Model Predictive Control Based Thrust Maximization of Linear Induction Motors Used in Linear Metros

Journal Article


Abstract


  • This paper proposes a finite set model predictive control (FS-MPC) based thrust maximization technique for linear induction machines used in linear metros. For modeling of the proposed control method, the end effect is taken into consideration. The proposed control method is used to achieve maximum thrust per ampere and to reduce the thrust ripples. It differs from the FS-MPC methods, where the cost function consists of the thrust and angle errors. The thrust error is calculated from the difference between the reference thrust and the predicted thrust, and the angle error is calculated from the difference between the angle of predicted primary current and the angle of the predicted secondary flux in one side and π/4 on the other side. A comparison between the proposed method and the finite set model predictive direct thrust control (FS-MPDTC) is presented to illustrate the superiority of the proposed method. Both simulation and experimental analysis are conducted to validate the effectiveness of the proposed finite set model predictive direct angle control (FS-MPDAC). A prototype test platform is developed in the laboratory with two 3 kW arc induction motors. The simulation model, experimental test platform, and test results are presented in this paper.

UOW Authors


  •   Xu, Wei (external author)
  •   Elmorshedy, Mahmoud (external author)
  •   Liu, Yi (external author)
  •   Islam, Md Rabiul
  •   Allam, Said (external author)

Publication Date


  • 2019

Citation


  • W. Xu, M. F. Elmorshedy, Y. Liu, M. Islam & S. M. Allam, "Finite-Set Model Predictive Control Based Thrust Maximization of Linear Induction Motors Used in Linear Metros," IEEE Transactions on Vehicular Technology, vol. 68, (6) pp. 5443-5458, 2019.

Scopus Eid


  • 2-s2.0-85067822670

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2922

Number Of Pages


  • 15

Start Page


  • 5443

End Page


  • 5458

Volume


  • 68

Issue


  • 6

Place Of Publication


  • United States

Abstract


  • This paper proposes a finite set model predictive control (FS-MPC) based thrust maximization technique for linear induction machines used in linear metros. For modeling of the proposed control method, the end effect is taken into consideration. The proposed control method is used to achieve maximum thrust per ampere and to reduce the thrust ripples. It differs from the FS-MPC methods, where the cost function consists of the thrust and angle errors. The thrust error is calculated from the difference between the reference thrust and the predicted thrust, and the angle error is calculated from the difference between the angle of predicted primary current and the angle of the predicted secondary flux in one side and π/4 on the other side. A comparison between the proposed method and the finite set model predictive direct thrust control (FS-MPDTC) is presented to illustrate the superiority of the proposed method. Both simulation and experimental analysis are conducted to validate the effectiveness of the proposed finite set model predictive direct angle control (FS-MPDAC). A prototype test platform is developed in the laboratory with two 3 kW arc induction motors. The simulation model, experimental test platform, and test results are presented in this paper.

UOW Authors


  •   Xu, Wei (external author)
  •   Elmorshedy, Mahmoud (external author)
  •   Liu, Yi (external author)
  •   Islam, Md Rabiul
  •   Allam, Said (external author)

Publication Date


  • 2019

Citation


  • W. Xu, M. F. Elmorshedy, Y. Liu, M. Islam & S. M. Allam, "Finite-Set Model Predictive Control Based Thrust Maximization of Linear Induction Motors Used in Linear Metros," IEEE Transactions on Vehicular Technology, vol. 68, (6) pp. 5443-5458, 2019.

Scopus Eid


  • 2-s2.0-85067822670

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2922

Number Of Pages


  • 15

Start Page


  • 5443

End Page


  • 5458

Volume


  • 68

Issue


  • 6

Place Of Publication


  • United States