同步电机无传感器控制

L. Veselý, P. Zbranek
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引用次数: 1

摘要

永磁同步电动机由于具有几个固有的优点,在许多工业应用中得到了应用。然而,有关转子位置的信息是必要的,甚至能够控制驱动速度。传统的速度和位置检测使用编码器和解析器。这些传感器显著增加了价格和重量,降低了可靠性。因此,许多作者发表了关于转子位置和转速估计算法的论文。一种状态估计的可能性是使用扩展卡尔曼滤波。传统的基于EKF的算法是基于一个简单的永磁同步电机模型开发的,因此在低速范围内性能较差。本文介绍了一种基于内置永磁同步电机模型的扩展卡尔曼滤波。这种模式使EKF在低速下运行成为可能。第二部分介绍了一种模型参考自适应系统(MRAS)算法。
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Sensorless control for synchronous motors
Permanent magnet synchronous motors are used in many industrial applications because they have several inherent advantages. However, information about the rotor position is necessary even to be able to control drive speed. Conventional speed and position detection uses encoder and resolver. These sensors significantly increase price, weight, and degrade reliability. Therefore many authors publish papers about algorithms for rotor position and speed estimation. One of state estimation possibility is using extended Kalman filter. Conventional algorithms based on EKF are developed using a simple model of permanent magnet synchronous motor, thereby provide bad performance in the low speed range. Extended Kalman filter using an interior permanent magnet synchronous motor model is described in this paper. This model makes possible for EKF to operate in low speeds. Second part of this paper describes an algorithm Model Reference Adaptive system (MRAS).
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