模型预测控制
开关磁阻电动机
控制工程
直接转矩控制
计算机科学
控制理论(社会学)
逆变器
噪音(视频)
机器控制
扭矩
灵活性(工程)
工程类
控制(管理)
感应电动机
转子(电动)
电压
人工智能
机械工程
统计
物理
电气工程
数学
图像(数学)
热力学
作者
José Rodríguez,Cristian Garcia,Andrés Mora,S. Alireza Davari,Jorge Rodas,Diego F. Valencia,Mahmoud F. 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 Dragičević,Davood Arab Khaburi,Zhen Zhang,Mohamed Abdelrahem,Nenad Mijatović
出处
期刊:IEEE Transactions on Power Electronics
[Institute of Electrical and Electronics Engineers]
日期:2022-05-01
卷期号:37 (5): 5047-5061
被引量:107
标识
DOI:10.1109/tpel.2021.3121589
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI