Neural Network Aided Unscented Kalman Filter for Sensorless Control of PMSM

被引：0

作者：

Talla, Jakub ^{[1
]}

Peroutka, Zdenek ^{[1
]}

机构：

[1] Univ W Bohemia, Reg Innovat Ctr Elect Engn, Plzen, Czech Republic

来源：

PROCEEDINGS OF THE 2011-14TH EUROPEAN CONFERENCE ON POWER ELECTRONICS AND APPLICATIONS (EPE 2011) | 2011年

关键词：

Permanent magnet motor; Sensorless control; Neural network; Estimation technique;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper introduces neural network aided unscented Kalman filter (NNUKF) for sensorless control of ac motor drives. Unscented Kalman filter (UKF) is completed by on-line trained neural network which compensates unmodeled dynamics and uncertainties of a drive model. This technique significantly improves behaviour of estimator in critical operating states, especially in low speeds.

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页数：9

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