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

Jose Rodriguez; Cristian Garcia; Andres Mora; S. Alireza Davari; Jorge Rodas; Diego Fernando Valencia; Mahmoud 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 Khaburi; Zhen Zhang; Mohamed Abdelrahem; Nenad Mijatovic

doi:10.1109/TPEL.2021.3121589

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

Jose Rodriguez
, Cristian Garcia
, Andres Mora
, S. Alireza Davari
, Jorge Rodas
, Diego Fernando Valencia
, Mahmoud 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 Khaburi

Zhen Zhang, Mohamed Abdelrahem, Nenad Mijatovic

University Andrés Bello
Universidad de Talca
Universidad Técnica Federico Santa María
Shahid Rajaee Teacher Training University
Universidad Nacional de Asunción
McMaster University, Faculty of Engineering
Faculty of Engineering
Chinese Academy of Sciences
Huazhong University of Science and Technology
North China Electric Power University
ABB
Technical University of Munich
Technical University of Denmark
Iran University of Science and Technology (IUST)
Tianjin Univ.

Research output: Contribution to journal › Article › peer-review

362 Scopus citations

Abstract

This article 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 article 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 language	English
Pages (from-to)	5047-5061
Number of pages	15
Journal	IEEE Transactions on Power Electronics
Volume	37
Issue number	5
DOIs	https://doi.org/10.1109/TPEL.2021.3121589
State	Published - 1 May 2022
Externally published	Yes

Keywords

Electric machine
predictive control
variable speed drives

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10.1109/TPEL.2021.3121589

Cite this

Rodriguez, J., Garcia, C., Mora, A., Davari, S. A., Rodas, J., Valencia, D. F., Elmorshedy, M., Wang, F., Zuo, K., Tarisciotti, L., Flores-Bahamonde, F., Xu, W., Zhang, Z., Zhang, Y., Norambuena, M., Emadi, A., Geyer, T., Kennel, R., Dragicevic, T., ... Mijatovic, N. (2022). Latest Advances of Model Predictive Control in Electrical Drives - Part II: Applications and Benchmarking With Classical Control Methods. IEEE Transactions on Power Electronics, 37(5), 5047-5061. https://doi.org/10.1109/TPEL.2021.3121589

@article{f9cb24e38cca4f9abd0cb62251845a7a,

title = "Latest Advances of Model Predictive Control in Electrical Drives - Part II: Applications and Benchmarking With Classical Control Methods",

abstract = "This article 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 article 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.",

keywords = "Electric machine, predictive control, variable speed drives",

author = "Jose Rodriguez and Cristian Garcia and Andres Mora and Davari, \{S. Alireza\} and Jorge Rodas and Valencia, \{Diego Fernando\} and Mahmoud Elmorshedy and Fengxiang Wang and Kunkun Zuo and Luca Tarisciotti and Freddy Flores-Bahamonde and Wei Xu and Zhenbin Zhang and Yongchang Zhang and Margarita Norambuena and Ali Emadi and Tobias Geyer and Ralph Kennel and Tomislav Dragicevic and Khaburi, \{Davood Arab\} and Zhen Zhang and Mohamed Abdelrahem and Nenad Mijatovic",

note = "Publisher Copyright: {\textcopyright} 1986-2012 IEEE.",

year = "2022",

month = may,

day = "1",

doi = "10.1109/TPEL.2021.3121589",

language = "English",

volume = "37",

pages = "5047--5061",

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Rodriguez, J, Garcia, C, Mora, A, Davari, SA, Rodas, J, Valencia, DF, Elmorshedy, M, Wang, F, Zuo, K, Tarisciotti, L, Flores-Bahamonde, F, Xu, W, Zhang, Z, Zhang, Y, Norambuena, M, Emadi, A, Geyer, T, Kennel, R, Dragicevic, T, Khaburi, DA, Zhang, Z, Abdelrahem, M & Mijatovic, N 2022, 'Latest Advances of Model Predictive Control in Electrical Drives - Part II: Applications and Benchmarking With Classical Control Methods', IEEE Transactions on Power Electronics, vol. 37, no. 5, pp. 5047-5061. https://doi.org/10.1109/TPEL.2021.3121589

TY - JOUR

T1 - Latest Advances of Model Predictive Control in Electrical Drives - Part II

T2 - Applications and Benchmarking With Classical Control Methods

AU - Rodriguez, Jose

AU - Garcia, Cristian

AU - Mora, Andres

AU - Davari, S. Alireza

AU - Rodas, Jorge

AU - Valencia, Diego Fernando

AU - Elmorshedy, Mahmoud

AU - Wang, Fengxiang

AU - Zuo, Kunkun

AU - Tarisciotti, Luca

AU - Flores-Bahamonde, Freddy

AU - Xu, Wei

AU - Zhang, Zhenbin

AU - Zhang, Yongchang

AU - Norambuena, Margarita

AU - Emadi, Ali

AU - Geyer, Tobias

AU - Kennel, Ralph

AU - Dragicevic, Tomislav

AU - Khaburi, Davood Arab

AU - Zhang, Zhen

AU - Abdelrahem, Mohamed

AU - Mijatovic, Nenad

PY - 2022/5/1

Y1 - 2022/5/1

N2 - This article 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 article 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.

AB - This article 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 article 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.

KW - Electric machine

KW - predictive control

KW - variable speed drives

UR - https://www.scopus.com/pages/publications/85118278523

U2 - 10.1109/TPEL.2021.3121589

DO - 10.1109/TPEL.2021.3121589

M3 - Article

AN - SCOPUS:85118278523

SN - 0885-8993

VL - 37

SP - 5047

EP - 5061

JO - IEEE Transactions on Power Electronics

JF - IEEE Transactions on Power Electronics

IS - 5

ER -

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

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Keywords

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