Neural network and fuzzy logic in a speed close loop for DTC induction motors

Author

Ponce, Pedro ; Molina, Arturo ; Tellez, Arturo

Author_Institution

Dept. de Mecatronica, ITESM-CCM, Mexico City, Mexico

fYear

2014

fDate

2-4 April 2014

Firstpage

1

Lastpage

7

Abstract

Direct Torque Control (DTC) is known to produce quick and robust response in AC drives. However, during steady state, torque, flux and current ripple occur. An improvement of the electric drive can be obtained using a DTC scheme based on the Space Vector Modulation (SVM) which reduces the torque and flux ripple. The proposed control scheme considers the rotor resistance variation. This paper also discusses the application of Type-2 Fuzzy speed Control under uncertain stimuli and an Artificial Neural Network (ANN) as a speed estimator. The capability and precision of this scheme as a speed controller and estimator are verified by different conditions and it is concluded that the proposed control scheme produces good results.

Keywords

angular velocity control; fuzzy control; induction motor drives; machine control; neural nets; torque control; ANN; DTC induction motors; SVM; artificial neural network; direct torque control; fuzzy logic; rotor resistance variation; space vector modulation; speed close loop; speed controller; speed estimator; type-2 fuzzy speed control; uncertain stimuli; Artificial neural networks; Fuzzy logic; Induction motors; Space vector pulse width modulation; Stators; Torque; Vectors; ANN; DTC; SVPWM; Sensorless; Type-2 Fuzzy Logic System;

fLanguage

English

Publisher

ieee

Conference_Titel

Devices, Circuits and Systems (ICCDCS), 2014 International Caribbean Conference on

Conference_Location

Playa del Carmen

Print_ISBN

978-1-4799-4684-6

Type

conf

DOI

10.1109/ICCDCS.2014.7016166

Filename

7016166