Automatic generation fuzzy neural network controller with supervisory control for permanent magnet linear synchronous motor

Abstract

The automatic generation fuzzy neural network (AGFNN) controller with supervisory control for permanent magnet linear synchronous motor (PMLSM) is proposed in this paper. It comprises an AGFNN controller, which has ability of rule automatic generation with on-line learning and a supervisory controller, which is designed to stabilize the system states around a bounded region. The Mahalanobis distance (M-distance) formula is employed that the neural network has the ability of identification of the rules will be generated or not. To improve the learning speed of back-propagation algorithm in AGFNN controller, a switching law and a momentum term are used in this study. The design of supervisory controller is derived in the Lyapunov sense; thus, the stability of the control system can be guaranteed. Finally, simulation results show that the proposed controller is robust with regard to plant parameter variations and external load disturbance.