Latest Advances of Model Predictive Control in Electrical Drives. Part II: Applications and Benchmarking with Classical Control Methods

Jose Rodriguez, Cristian Garcia, Andres Mora, Alireza Davari, Jorge Rodas, Diego Fernando Valencia Garcia, Mahmoud Fouad Elmorshedy, Fengxiang Wang, Kunkun Zuo, Luca Tarisciotti, Freddy Flores-Bahamonde, Wei Xu, Zhenbin Zhang, Yongchang Zhang, Margarita Norambuena, Ali Emadi, Tobias Geyer, Ralph Kennel, Tomislav Dragicevic, Davood Arab KhaburiZhen Zhang, Mohamed Abdelrahem, Nenad Mijatovic

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Abstract

This paper presents the application of Model Predictive Control (MPC) in high-performance drives. A wide variety of machines have been considered: induction machines, synchronous machines, linear motors, switched reluctance motors, and multiphase machines. The control of these machines has been done by introducing minor and easy-to-understand modifications to the basic predictive control concept, showing the high flexibility and simplicity of the strategy. The second part of the paper is dedicated to the performance comparison of MPC with classical control techniques such as field-oriented control and direct torque control. The comparison considers the dynamic behavior of the drive and steady-state performance metrics such as inverter losses, current distortion in the motor, and acoustic noise. The main conclusion is that MPC is very competitive concerning classic control methods by reducing the inverter losses and the current distortion with comparable acoustic noise.
Original languageEnglish
JournalIEEE Transactions on Power Electronics
Number of pages15
ISSN0885-8993
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Control systems
  • Induction motors
  • Inverters
  • Predictive control
  • Predictive models
  • Robustness
  • Torque
  • variable speed drives

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