Research on Flux Observer Based on Wavelet Neural Network Adjusted by Ant Colony 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 time characteristics of wavelet neural network flux observer, the paper applies ant colony algorithm (ACA) with embedded deterministic searching strategy to optimize dilation factor, translation factor and output weight of wavelet neural network. In order to confirm on-line identification precision of wavelet neural network flux observer based on ant colony algorithm, 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