Modulated Model Predictive Speed Controller for PMSM Drives Employing Voltage-Based Cost Function

IF 5.2 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Open Journal of the Industrial Electronics Society Pub Date : 2024-02-22 DOI:10.1109/OJIES.2024.3368568

Ahmed Aboelhassan;Shuo Wang;Giampaolo Buticchi;Vasyl Varvolik;Michael Galea;Serhiy Bozhko

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引用次数: 0

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 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.

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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

各种电气传动系统广泛采用了经典的级联现场导向控制（FOC）拓扑结构，包括速度环、电流环和调制。另一方面，为了获得更快的动态响应和更好的电能质量，最近在不同的应用中采用了调制模型预测控制（M2PC）。FOC 拓扑的速度和电流控制环路可以合并，以简化控制系统结构并改善系统动态。因此，本文介绍了一种采用 M2PC 的永磁同步电机非级联速度环控制器。通过所需的仿真工作，分析了与比例积分 (PI)、非级联模型预测控制和 M2PC 控制器相比的算法性能。此外，该算法还通过专用试验台进行了实际应用，研究结果表明了该算法的优点，包括谐波含量、动态行为和对参数失配的鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

10.80

自引率

2.40%

发文量

审稿时长

12 weeks

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.