RADIAL BASIS FUNCTION NETWORK BASED AUTOMATIC GENERATION FUZZY NEURAL NETWORK CONTROLLER FOR PERMANENT MAGNET LINEAR SYNCHRONOUS MOTOR

In this paper, a radial basis function network (RBFN) based automatic generation fuzzy neural network (AGFNN) controller is proposed to control the rotor position of the permanent magnet linear synchronous motor (PMLSM) to track the period reference trajectories The proposed scheme has not only the advantages of the back-propagation algorithm, in which the parameters of the connected weights are adjusted, but also has advantages of the switching law, momentum term, and RBFN, In which the tracking error and steady state responses will be improved. The structure learning is based on the Mahalanobis distance and the parameter learning is based on the back-propagation algorithm. The simulation results of the proposed controller with the periodic reference trajectories show that the tracking error and steady state responses have the satisfactory performance and own the robustness performance under the parameter variation and external load disturbance.