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Accurate Torque Control for Induction Motors by Utilizing a Globally Optimized Flux Observer
IEEE Transactions on Power Electronics ( IF 6.7 ) Pub Date : 2021-05-13 , DOI: 10.1109/tpel.2021.3080129
Marius Stender , Oliver Wallscheid , Joachim Bocker

High-precision torque estimation and control of induction motor drives is an important research field due to the extensive use of these motors in torque controlled applications, for example, in electric vehicles. The open-loop torque control performance depends strongly on the accuracy of both the motor model and the flux estimation. To address this appropriately, an adaptive Kalman filter with offline parameter and observer design optimization is proposed in this article. Thereby, the basic induction motor model is extended by magnetic saturation, iron losses, and skin effect influences. All uncertain motor model and observer configuration parameters are offline identified by a global optimization technique, namely particle swarm optimization. The identification utilizes a comprehensive data set consisting of test bench measurements and leads to an optimized observer enabling precise torque estimation and control. In experimental validation, for both torque estimation and control, the root-mean-square error is below 1 % of the nominal torque over the entire operating range. With the help of an accurate gray-box inverter model for phase voltage estimation and a speed-adaptive Kalman filter tuning scheme, the proposed observer is able to operate also at slow speeds including standstill.

中文翻译:

使用全局优化的磁通观测器精确控制感应电机的转矩

感应电机驱动器的高精度扭矩估计和控制是一个重要的研究领域,因为这些电机广泛用​​于扭矩控制应用,例如电动汽车。开环转矩控制性能在很大程度上取决于电机模型和磁通估计的准确性。为了适当地解决这个问题,本文提出了一种具有离线参数和观测器设计优化的自适应卡尔曼滤波器。因此,基本感应电机模型通过磁饱和、铁损和趋肤效应影响进行扩展。所有不确定的电机模型和观测器配置参数都通过全局优化技术离线识别,即粒子群优化。识别利用由测试台测量组成的综合数据集,并导致优化的观测器能够实现精确的扭矩估计和控制。在实验验证中,对于扭矩估计和控制,均方根误差在整个操作范围内低于标称扭矩的 1%。借助用于相电压估计的准确灰盒逆变器模型和速度自适应卡尔曼滤波器调谐方案,所提出的观察器也能够以低速运行,包括静止。
更新日期:2021-05-13
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