Abstract: The cogging force is of great impact to the efficiency of permanent magnetic liner synchronous motor(PMLSM) es-pecially in high precision and low speed. According to the frac-tional slot with q<1 structure of PMLSM, FEM is used to ana-lyze the influence of cogging force. Supposed estimator based on radical basis functional network(RBFN) is presented by improved algorithm. To select the right spread factor of base function, the accelerate fuzzy C-means(AFCM) is used in data clustering. Then, OLSA is used to choose the center vector from the clustering center. Comparing to the estimator based on back propagation neural network(BPNN) with momentum method, the novel estimator increases the clustering of neural network with boosting learning rate. Results show the fractional slot with q <1 structure effectively reduces the influence of cogging force in PMLSM. Through the estimator based on RBFN, the parameters of the PMLSM can be evaluated in the design pe-riod. By satisfying the standards of cogging force ripple, the estimator achieves the agility demand and improves the design level of PMLSM.

Key words: Accelerated fuzzy C-means, Orthogonal least squares learning algorithm, Permanent magnet linear synchronous motor, Radial basis function network