|
Abstract: The
cogging force is of great impact to the efficiency of permanent magnetic
liner synchronous motor(PMLSM) especially in high precision and low
speed. According to the fractional slot with q<1 structure of PMLSM, FEM is used to analyze 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 period. By
satisfying the standards of cogging force ripple, the estimator achieves
the agility demand and improves the design level of PMLSM.
Key words: Permanent magnet linear synchronous motor
Accelerated fuzzy C-means
Orthogonal least squares learning algorithm
Radial basis function network
CLC No: TM359.4
国家自然科学基金资助项目(50475101). Received 20060213, received in revised form 20060924
|