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....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-01-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/00051144.2018.1486796 |
| Summary: | 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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| ISSN: | 0005-1144 1848-3380 |