Direct torque control based on FNN and optimization

Author

Cao, Chengzhi ; Wang, Xin ; Lu, Mu-Ping

Author_Institution

Sch. of Inf. Sci. & Eng., Shenyang Technol. Univ., Shengyang, China

Volume

2

fYear

2003

fDate

2-5 Nov. 2003

Firstpage

760

Abstract

The paper is proposed for fuzzy neural net based on genetic algorithm adjusting asynchronous motor direct torque control system. Fuzzy neural network training usually adopts the back-propagation study algorithm (BP). Yet the training time of BP algorithm is longer and it is easy to fall into problems such as local minima and so on. We adopt the genetic algorithm in order to optimize the weight of the fuzzy neural network and the parameters of the membership function. We make up for the defect of the fuzzy neural network by adopting the BP algorithm. This made the speed of the arithmetic faster and helped to achieve global optimization.

Keywords

backpropagation; fuzzy neural nets; genetic algorithms; induction motors; torque control; asynchronous motor; backpropagation; direct torque control system; fuzzy neural net; genetic algorithm; global optimization; membership function; network training; Control systems; Fuzzy control; Fuzzy neural networks; Genetic algorithms; Intelligent control; Neural networks; Regulators; Stators; Switches; Torque control;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2003 International Conference on

Print_ISBN

0-7803-8131-9

Type

conf

DOI

10.1109/ICMLC.2003.1259578

Filename

1259578