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arXiv 2402.05263eess.SYcs.SY
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无传感器技术的无刷直流电机位置与速度控制及其应用趋势

Position and Speed Control of Brushless DC Motors Using Sensorless Techniques and Application Trends

Jose-Carlos Gamazo-Real, Ernesto Vazquez-Sanchez, Jaime Gomez-Gil

AI总结:

本文综述了无刷直流电机无传感器位置与速度控制方法,涵盖反电动势传感及估计技术,并分析其优缺点与应用趋势。

摘要:

本文对无刷直流(BLDC)电机驱动器的位置和速度无传感器控制方法进行了技术综述,包括使用传感器的背景分析、局限性及进展。由于通过无传感器技术改进了传统控制和传感技术,BLDC电机驱动器的性能和可靠性得到了提升。随后,本文回顾了无传感器技术的进展,并介绍了该领域的最新发展及其固有的优缺点,包括实际实现问题和应用的分析。研究深入概述了最先进的反电动势(back-EMF)传感方法,包括端子电压传感、三次谐波电压积分、端子电流传感、反电动势积分和PWM策略。此外,简要分析了基于估计和模型的最相关技术,如滑模观测器、扩展卡尔曼滤波器、模型参考自适应系统、自适应观测器(全阶和伪降阶)以及人工神经网络。

英文摘要:

This paper provides a technical review of position and speed sensorless methods for controlling Brushless Direct Current (BLDC) motor drives, including the background analysis using sensors, limitations and advances. The performance and reliability of BLDC motor drivers have been improved because the conventional control and sensing techniques have been improved through sensorless technology. Then, in this paper sensorless advances are reviewed and recent developments in this area are introduced with their inherent advantages and drawbacks, including the analysis of practical implementation issues and applications. The study includes a deep overview of state-of-the-art back-EMF sensing methods, which includes Terminal Voltage Sensing, Third Harmonic Voltage Integration, Terminal Current Sensing, Back-EMF Integration and PWM strategies. Also, the most relevant techniques based on estimation and models are briefly analysed, such as Sliding-mode Observer, Extended Kalman Filter, Model Reference Adaptive System, Adaptive observers (Full-order and Pseudoreduced-order) and Artificial Neural Networks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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