Abstract:
For the chaotic system of Permanent Magnet Synchronous Motor, the RBF-GWO network chaotic synchronization controllers were designed based on Grey Wolf Optimizer (GWO) and its several variant algorithms. In RBF-GWO, RBF neural network structure was adopted, the combination of the hidden center matrix, Gaussian RMS width vector and the hidden-output weight matrix were assumed as the position vector of grey wolf. Half of the average squared error was selected as the optimizing object function. The difference between the actual output and the desired one was considered as the guide of updating the position vector for the grey wolf, and in each iteration the optimal parameters were stored in the position vector of wolf α which would be returned to the RBF-GWO until the end condition of the iteration was satisfied. Through the chaotic homogeneous and heterogeneous synchronization control experiments, it was proved that the proposed RBF-GWO network was effective, and it was found that the one network based on the WGWO had relatively stronger adaptive capacity than others.