Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motor

This paper discusses about self-regulating the reference flux in induction motor (IM) direct torque control (DTC) drive by fuzzy logic. Self-regulation is improved by using “Artificial Neural Network (ANN)” and “Adaptive Network Based Fuzzy Inference System (ANFIS)” based reference flux estimators....

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Main Authors: S. Sampath Kumar, R. Joseph Xavier, S. Balamurugan
Format: Article
Language:English
Published: Taylor & Francis Group 2018-01-01
Series:Automatika
Subjects:
Online Access:http://dx.doi.org/10.1080/00051144.2018.1486796
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author S. Sampath Kumar
R. Joseph Xavier
S. Balamurugan
author_facet S. Sampath Kumar
R. Joseph Xavier
S. Balamurugan
author_sort S. Sampath Kumar
collection DOAJ
description This paper discusses about self-regulating the reference flux in induction motor (IM) direct torque control (DTC) drive by fuzzy logic. Self-regulation is improved by using “Artificial Neural Network (ANN)” and “Adaptive Network Based Fuzzy Inference System (ANFIS)” based reference flux estimators. Furthermore, PI speed controller is investigated to develop the performance of the drive. Two different PI speed controller tuning strategies, manual and Fuzzy Gain Scheduling (FGS), are compared for load torque disturbance. The results clearly show that the modified DTC of IM with “ANFIS-based reference flux estimator and FGS-tuned PI speed controller” is most suitable for torque ripple reduction and speed control.
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spelling doaj.art-16c05b29727c4997a9371fa89b6902842022-12-22T01:28:08ZengTaylor & Francis GroupAutomatika0005-11441848-33802018-01-01591112310.1080/00051144.2018.14867961486796Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motorS. Sampath Kumar0R. Joseph Xavier1S. Balamurugan2Amrita School of EngineeringSri Ramakrishna Institute of TechnologyAmrita School of EngineeringThis paper discusses about self-regulating the reference flux in induction motor (IM) direct torque control (DTC) drive by fuzzy logic. Self-regulation is improved by using “Artificial Neural Network (ANN)” and “Adaptive Network Based Fuzzy Inference System (ANFIS)” based reference flux estimators. Furthermore, PI speed controller is investigated to develop the performance of the drive. Two different PI speed controller tuning strategies, manual and Fuzzy Gain Scheduling (FGS), are compared for load torque disturbance. The results clearly show that the modified DTC of IM with “ANFIS-based reference flux estimator and FGS-tuned PI speed controller” is most suitable for torque ripple reduction and speed control.http://dx.doi.org/10.1080/00051144.2018.1486796Direct torque controlfuzzy logicANNANFISfuzzy gain schedulingPI controller
spellingShingle S. Sampath Kumar
R. Joseph Xavier
S. Balamurugan
Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motor
Automatika
Direct torque control
fuzzy logic
ANN
ANFIS
fuzzy gain scheduling
PI controller
title Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motor
title_full Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motor
title_fullStr Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motor
title_full_unstemmed Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motor
title_short Development of ANFIS-based reference flux estimator and FGS-tuned speed controller for DTC of induction motor
title_sort development of anfis based reference flux estimator and fgs tuned speed controller for dtc of induction motor
topic Direct torque control
fuzzy logic
ANN
ANFIS
fuzzy gain scheduling
PI controller
url http://dx.doi.org/10.1080/00051144.2018.1486796
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AT sbalamurugan developmentofanfisbasedreferencefluxestimatorandfgstunedspeedcontrollerfordtcofinductionmotor