Speed estimation of an induction motor drive using an optimized extended Kalman filter
Speed estimation of an induction motor drive using an optimized extended Kalman filter
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DOI：
10.1109/41.982256
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发表时间：
2002-08
期刊：
影响因子：
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通讯作者：
K. Shi;T. Chan;Y. Wong;S. Ho
中科院分区：
文献类型：
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作者：
K. Shi;T. Chan;Y. Wong;S. Ho
本文提出了一种新的方法，以实现良好的性能的扩展卡尔曼滤波器（EKF）的速度估计的感应电机驱动器。一个实数编码的遗传算法（GA）被用来优化噪声协方差和权重矩阵的EKF，从而确保滤波器的稳定性和速度估计的准确性。对恒V/Hz控制器和磁场定向控制器（FOC）在各种运行条件下的仿真研究证明了所提出的方法的有效性。实验系统包括一个原型的数字信号处理器为基础的FOC感应电动机驱动器的硬件设施，用于获取速度，电压和电流信号到PC机。包括离线GA训练和验证阶段的实验，以验证优化的EKF的性能。
This paper presents a novel method to achieve good performance of an extended Kalman filter (EKF) for speed estimation of an induction motor drive. A real-coded genetic algorithm (GA) is used to optimize the noise covariance and weight matrices of the EKF, thereby ensuring filter stability and accuracy in speed estimation. Simulation studies on a constant V/Hz controller and a field-oriented controller (FOC) under various operating conditions demonstrate the efficacy of the proposed method. The experimental system consists of a prototype digital-signal-processor-based FOC induction motor drive with hardware facilities for acquiring the speed, voltage, and current signals to a PC. Experiments comprising offline GA training and verification phases are presented to validate the performance of the optimized EKF.