Application of Fuzzy Neural Network in Direct Torque Control System

Author

Liping Fan ; Bin Li

Author_Institution

Shenyang Inst. of Chem. Technol., Shenyang

fYear

2007

fDate

May 30 2007-June 1 2007

Firstpage

2186

Lastpage

2191

Abstract

Induction motors have some inherent characteristics such as multivariate, parameter indeterminacy, strong coupling and non-linearity. These bring about a lot of trouble to the induction motor drive system. Considering the problems of AC speed regulation cause by the motor´s inherent characteristics, a fuzzy control strategy based on the RBF neural network was presented. It was designed to make full use of the features of RBF neural network and fuzzy control. A contrastive research was made between the conventional direct torque control system and the fuzzy control system based on RBF. Simulation results show that the fuzzy control strategy based on RBF for direct torque control System has strong robustness, quick response, low overshot.

Keywords

angular velocity control; control engineering computing; electric machine analysis computing; fuzzy control; fuzzy neural nets; induction motor drives; machine control; radial basis function networks; torque control; AC speed regulation; RBF neural network; direct torque control system; fuzzy control; fuzzy neural network; induction motor drive system; parameter indeterminacy; Artificial neural networks; Automation; Control systems; Error correction; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Neural networks; Stators; Torque control; RBF neural network; direct torque control; fuzzy control; stator Resistance Observer;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2007. ICCA 2007. IEEE International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4244-0817-7

Type

conf

DOI

10.1109/ICCA.2007.4598816

Filename

4598816