Research on Flux Observer Based on Wavelet Neural Network Adjusted by Antcolony Optimization

To improve the performance of extra-low speed in direct torque control (DTC) system, this paper applies wavelet neural network (WNN) to constitute flux observer by deep researching nonlinear mathematic model of stator flux of asynchronous motor. Furthermore, in order to improve rapidity and real tim...

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Bibliographic Details
Published in2007 International Conference on Machine Learning and Cybernetics Vol. 2; pp. 862 - 866
Main Authors Cheng-Zhi Cao, Xiao-Feng Guo, Wen-Jing Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Ant colony algorithm
Ant colony optimization
Control systems
Cybernetics
Discrete wavelet transforms
Flux observer
Information science
Machine learning
Neural networks
Pulse width modulation inverters
Stators
Torque control
Wavelet neural network
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ISBN1424409721
9781424409723
ISSN2160-133X
DOI10.1109/ICMLC.2007.4370263

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Summary:To improve the performance of extra-low speed in direct torque control (DTC) system, this paper applies wavelet neural network (WNN) to constitute flux observer by deep researching nonlinear mathematic model of stator flux of asynchronous motor. Furthermore, in order to improve rapidity and real time characteristics of WNN flux observer, the paper applies ant colony algorithm (ACA) with embedded deterministic searching strategy to optimize dilation factor, translation factor and output weight of WNN. The paper compares this method with wavelet neural network flux observer optimized by gradient descent algorithm. Simulation shows that the former not only can reduce the node numbers of hidden layers and quicken the convergence rate of WNN, but also can improve on-line identification precision of flux observer, so it can effectively improve low speed performance of DTC system.
ISBN:1424409721
9781424409723
ISSN:2160-133X
DOI:10.1109/ICMLC.2007.4370263