Ahmed Aboelhassan;Shuo Wang;Giampaolo Buticchi;Vasyl Varvolik;Michael Galea;Serhiy Bozhko
{"title":"采用基于电压的成本函数的 PMSM 驱动器调制模型预测速度控制器","authors":"Ahmed Aboelhassan;Shuo Wang;Giampaolo Buticchi;Vasyl Varvolik;Michael Galea;Serhiy Bozhko","doi":"10.1109/OJIES.2024.3368568","DOIUrl":null,"url":null,"abstract":"Various electrical drive systems have widely implemented the classical cascaded field-oriented control (FOC) topology, including speed loop, current loop, and modulation. On the other hand, modulated model predictive control (M\n<sup>2</sup>\nPC) has been employed recently for different applications for faster dynamic response and better power quality. The FOC topology's speed and current control loops can be merged to simplify the control system structure and improve the system dynamics. Therefore, a noncascaded speed loop controller employing M\n<sup>2</sup>\nPC for permanent magnet synchronous motors is introduced. The required simulation work has been developed to analyze the algorithm performance compared to proportional integral (PI), noncascaded model predictive control, and M\n<sup>2</sup>\nPC controllers. In addition, it has been applied practically through a dedicated testing rig, and results are investigated showing its merits including harmonic content, dynamic behavior, and robustness against parameter mismatch.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":"5 ","pages":"122-131"},"PeriodicalIF":5.2000,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443480","citationCount":"0","resultStr":"{\"title\":\"Modulated Model Predictive Speed Controller for PMSM Drives Employing Voltage-Based Cost Function\",\"authors\":\"Ahmed Aboelhassan;Shuo Wang;Giampaolo Buticchi;Vasyl Varvolik;Michael Galea;Serhiy Bozhko\",\"doi\":\"10.1109/OJIES.2024.3368568\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Various electrical drive systems have widely implemented the classical cascaded field-oriented control (FOC) topology, including speed loop, current loop, and modulation. On the other hand, modulated model predictive control (M\\n<sup>2</sup>\\nPC) has been employed recently for different applications for faster dynamic response and better power quality. The FOC topology's speed and current control loops can be merged to simplify the control system structure and improve the system dynamics. Therefore, a noncascaded speed loop controller employing M\\n<sup>2</sup>\\nPC for permanent magnet synchronous motors is introduced. The required simulation work has been developed to analyze the algorithm performance compared to proportional integral (PI), noncascaded model predictive control, and M\\n<sup>2</sup>\\nPC controllers. In addition, it has been applied practically through a dedicated testing rig, and results are investigated showing its merits including harmonic content, dynamic behavior, and robustness against parameter mismatch.\",\"PeriodicalId\":52675,\"journal\":{\"name\":\"IEEE Open Journal of the Industrial Electronics Society\",\"volume\":\"5 \",\"pages\":\"122-131\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2024-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443480\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of the Industrial Electronics Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10443480/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10443480/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Modulated Model Predictive Speed Controller for PMSM Drives Employing Voltage-Based Cost Function
Various electrical drive systems have widely implemented the classical cascaded field-oriented control (FOC) topology, including speed loop, current loop, and modulation. On the other hand, modulated model predictive control (M
2
PC) has been employed recently for different applications for faster dynamic response and better power quality. The FOC topology's speed and current control loops can be merged to simplify the control system structure and improve the system dynamics. Therefore, a noncascaded speed loop controller employing M
2
PC for permanent magnet synchronous motors is introduced. The required simulation work has been developed to analyze the algorithm performance compared to proportional integral (PI), noncascaded model predictive control, and M
2
PC controllers. In addition, it has been applied practically through a dedicated testing rig, and results are investigated showing its merits including harmonic content, dynamic behavior, and robustness against parameter mismatch.
期刊介绍：
The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments.
Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.
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