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Performance evaluation of nonlinear Kalman filtering techniques in low speed brushless DC motors driven sensor-less positioning systems

机译：低速无刷直流电动机驱动的无传感器定位系统中非线性卡尔曼滤波技术的性能评估

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摘要

Appropriate position estimation of electric actuator is one of the most important stages in the control and automation processes. Considering various applications including robotics and automotive systems, Brushless DC (BLDC) motors sensor-less positioning is substantial in two major aspects. Electric commutation on the electric side should be performed precisely as well as motion control on the mechanical side. The mathematical models which befit precise applications are inherently nonlinear and require specific control techniques to deal with. Nonlinear Kalman filtering methods are considered as suitable solutions to estimation problems where uncertainty and noise exist. Three major and basic algorithms are Extended (EKF), Unscented (UKF) and Cubature Kalman filtering (CKF). In this paper, the application of these methods in estimation of rotor angular position with emphasis on low speed state is presented. Performance measures are compared using experimental setup. A typical 3-phase low voltage BLDC motor is implemented in the setup so that system noise could deteriorate quality of Back-EMF signal in low speed mode. It is shown that UKF and CKF techniques yield better results and performance in comparison to EKF according to measures. Estimated model states diagrams indicate the superior performance of Unscented and Cubature types regarding both accuracy and convergence.
机译：电动执行器的适当位置估计是控制和自动化过程中最重要的阶段之一。考虑到包括机器人技术和汽车系统在内的各种应用，无刷直流（BLDC）电机无传感器定位在两个主要方面很重要。电气方面的电气换向以及机械方面的运动控制均应精确执行。适合精确应用的数学模型本质上是非线性的，需要特定的控制技术来处理。非线性卡尔曼滤波方法被认为是解决存在不确定性和噪声的估计问题的合适解决方案。三种主要的基本算法是扩展（EKF），无味（UKF）和Cubature卡尔曼滤波（CKF）。本文介绍了这些方法在以低速状态为重点的转子角位置估计中的应用。使用实验设置比较性能指标。该设置中采用了典型的三相低压BLDC电动机，因此系统噪声可能会降低低速模式下反电动势信号的质量。结果表明，根据测量结果，与EKF相比，UKF和CKF技术可产生更好的结果和性能。估计的模型状态图显示了Unscented和Cubature类型在准确性和收敛性方面的优越性能。

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