An Improved Stator Resistance Adaptation Mechanism in MRAS Estimator for Sensorless Induction Motor Drives

被引：1

作者：

Krishna, S. Mohan ^{[1
]}

Daya, J. L. Febin ^{[1
]}

机构：

[1] VIT Univ, Sch Elect Engn, Chennai, Tamil Nadu, India

来源：

PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND COMMUNICATION | 2017年 / 458卷

关键词：

Speed estimation; Adaptive control; Model reference; Machine model; Computational intelligence; VECTOR CONTROL; ROTOR FLUX; IDENTIFICATION; MACHINES; OBSERVER; SCHEME;

D O I：

10.1007/978-981-10-2035-3_38

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A comparative study of the conventional fixed gain PI and Fuzzy Logic based adaptation mechanisms for estimating the stator resistance in a Model Reference Adaptive System (MRAS) based sensorless induction motor drive is investigated here. The rotor speed is estimated parallely by means of a PI control based adaptive mechanism and the electromagnetic torque is also estimated to add more resilience. By considering the external Load torque perturbation as a model perturbation on the estimated stator resistance, the effects of the same on the estimated parameters are observed. The superiority of the Fuzzy based stator resistance adaptation mechanism is observed through detailed simulation performed offline using Matlab/Simulink blocksets. Furthermore, a sensitivity analysis of the stator resistance estimate with respect to load torque is also done to verify the effectiveness of the above concept.

引用

页码：371 / 385

页数：15

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