Using artificial neural networks in the induction motor DTC scheme

Author

Ponce, Pedro ; Aguilar, Daniel M. ; Monroy, Alfonso

Author_Institution

Inst. Tecnologico y de Estudios Superiores de Monterrey, Mexico

Volume

5

fYear

2004

fDate

20-25 June 2004

Firstpage

3325

Abstract

The purpose of this paper is to show the potential use of artificial neural networks (ANNs) in the induction motor direct torque control (DTC) scheme. The speed estimators using ANNs are contrasted with the speed adaptive flux observer (Luenberger observer). The paper proposes a complete DTC scheme using two different ANNs to estimate the rotor speed, one of them is a classical feedforward neural network (FFNN) and the other one is a FFNN chosen by genetic algorithms. The performance of the proposed scheme is carried out by simulation tests using MATLAB/SIMULINK.

Keywords

angular velocity control; electric machine analysis computing; feedforward neural nets; genetic algorithms; induction motors; machine control; observers; rotors; torque control; ANN; Luenberger observer; MATLAB/SIMULINK; artificial neural networks; direct torque control; feedforward neural network; genetic algorithms; induction motor; rotor speed estimators; speed adaptive flux observer; Artificial neural networks; DC motors; Genetic algorithms; Hysteresis motors; Induction motors; Intelligent networks; Neural networks; Sensorless control; Stators; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics Specialists Conference, 2004. PESC 04. 2004 IEEE 35th Annual

ISSN

0275-9306

Print_ISBN

0-7803-8399-0

Type

conf

DOI

10.1109/PESC.2004.1355063

Filename

1355063