Online robust estimation of flux and load torque in induction motors

被引：0
作者：
Mohamed Bahloul
Larbi Chrifi-Alaoui
Alessandro N. Vargas
Mohamed Chaabane
Said Drid
机构：
[1] Tyndall National Institute,International Energy Research Centre (IERC)
[2] Laboratory of Science and technique of Automatic Control and Computer Engineering (Lab-STA) National Engineering School of Sfax,Laboratory of Innovative Technology
[3] University of Picardie Jules Verne,Laboratory of induction and propulsion systems
[4] Universidade Tecnológica Federal do Paraná - UTFPR,undefined
[5] University of Batna,undefined
来源：
The International Journal of Advanced Manufacturing Technology | 2018年 / 94卷
关键词：
Robust load torque identification; Fuzzy adaptive observer; technique; Induction machine;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper presents a comparative study between two methods dedicated to the robust estimation of load torque and flux of induction motors (IM). The developed approaches rely on the adaptive Luenberger observer theory. The first method is based on the development of a Takagi-Sugeno Adaptive Luenberger Observer. In order to enhance the dynamic of the load torque estimation, a second method is presented using a Takagi-Sugeno Fast Adaptive Luenberger Observer approach. Sufficient conditions are presented to ensure the asymptotic convergence of the flux and the load torque estimation errors. Moreover, robustness performances are considered in order to minimize the impact of the rotor resistance variations on the quality of the estimation. Experiments were carried out to illustrate the effectiveness and the robustness of the proposed results and to show the advantages and limitations of each method.
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页码：2703 / 2713
页数：10
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