Abstract
ABSTRACTThis 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.
Highlights
Induction motors (IMs) are extensively used in industry due to their cost-effectiveness, reliability and robustness to load variations [1,2,3,4,5]
Artificial Neural Network (ANN) and Adaptive Network Based Fuzzy Inference System (ANFIS)-based reference flux estimators are developed for direct torque control (DTC)
These ANN and ANFIS-based reference flux estimators are attempted which have the relation between voltages and currents in matrix form using equivalent circuit of induction machine as not been tried by other researchers so far
Summary
Induction motors (IMs) are extensively used in industry due to their cost-effectiveness, reliability and robustness to load variations [1,2,3,4,5]. DTC directly controls the torque and stator flux of the IM by adjusting the inverter switching signals. The stator flux reference value is computed based on command torque by using fuzzy logic [16]. The fuzzy logic controller inputs are error in torque, stator flux reference value and the output is the modified reference flux. ANN and ANFIS-based reference flux estimators are developed for DTC These ANN and ANFIS-based reference flux estimators are attempted which have the relation between voltages and currents in matrix form using equivalent circuit of induction machine as not been tried by other researchers so far. This overcomes the disadvantage of yielding same gain values even for large changes in speed
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