Fuzzy Adaptive Neural-Network Model-Following Speed Control for PMSM Drives

In this paper, a fuzzy adaptive neural-network model-following speed controller for permanent-magnet synchronous motor (PMSM) drives is proposed. The fuzzy neuralnetwork model-following controller (FNNMFC) consist of a proportional plus integral (PI) like-fuzzy controller in addition to an on-line trained neural-network model-following controller (NNMFC). This controller, FNNMFC, combines the merits of the fuzzy logic control (FLC) and the neural-network model-following control (NNMFC) for PMSM drive. The weights of the NNMFC are trained on-line to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the fuzzy speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed FNNMF speed controller. The results confirm that the proposed FNNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations. Key-Words: PMSM, Vector Control, Fuzzy Logic, Neural Network (NN), Model Following Controller (MFC).