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MTPA-Based Sequential Model Predictive Control of Induction Motors
ID：119 View Protection：ATTENDEE Updated Time：2025-05-06 15:16:24 Hits：279 Oral

Start Time：Pending（Asia/Shanghai）

Duration：Pending

Session：No Session »

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Abstract
Abstract—Recently, sequential model predictive control (SMPC) has been proposed, eliminating the necessity of weighting factors by exploiting the hierarchical structure of cost functions. In conventional SMPC, to meet all load conditions, flux reference is set to the nominal value of the induction motor, leading to suboptimal operation. This paper incorporates Maximum Torque Per Ampere (MTPA) principles with SMPC to generate a proper flux reference. By doing so, the stator current amplitude and consequently the losses are reduced in light load conditions. Moreover, the number of voltage vectors (VVs) selected by the first cost function varies between two and three according to the load condition. Finally, the simulation results demonstrate the effectiveness of the proposed method in stator current amplitude and loss reduction.
 
Keywords
Sequential MPC,MODEL PREDICTIVE CONTROL,Model Predictive Torque Control (MPTC),MTPA,Induction motor
Speaker
Jose Rodriguez
Professor Universidad San Sebastian

Submission Author
Ali Haddadi Iran University of Science and Technology
Mahdi Bahmani Iran University of Science and Technology
Davood Arab Khaburi Iran University of Science and Technology
Cristian Garcia Universidad de Talca
Jose Rodriguez Universidad San Sebastian
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Important Date

  • Conference Date

    Jun 05

    2025

    to

    Jun 01

    2026

  • May 30 2025

    Draft paper submission deadline

  • Jun 08 2025

    Registration deadline

Sponsored By
China Southeast University
IEEE Power Electronics Society
Jiangsu Association of Automation
Nanjing Section IE Chapter
Organized By
Southeast University
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