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

Abstract

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.