TY - JOUR
T1 - Latest Advances of Model Predictive Control in Electrical Drives. Part II: Applications and Benchmarking with Classical Control Methods
AU - Rodriguez, Jose
AU - Garcia, Cristian
AU - Mora, Andres
AU - Davari, Alireza
AU - Rodas, Jorge
AU - Valencia Garcia, Diego Fernando
AU - Elmorshedy, Mahmoud Fouad
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 - Arab Khaburi, Davood
AU - Zhang, Zhen
AU - Abdelrahem, Mohamed
AU - Mijatovic, Nenad
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Control systems
KW - Induction motors
KW - Inverters
KW - Predictive control
KW - Predictive models
KW - Robustness
KW - Torque
KW - variable speed drives
U2 - 10.1109/TPEL.2021.3121589
DO - 10.1109/TPEL.2021.3121589
M3 - Journal article
SN - 0885-8993
VL - 37
SP - 5047
EP - 5061
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 5
ER -