A RBFN Based Cogging Force Estimator of PMLSM with q<1 Structure
The cogging force has great impact to the efficiency of permanent magnetic liner synchronous motor (PMLSM) especially at high precision and low speed. This paper presents a cogging force estimator based on radical basis functional network (RBFN) by accelerated fuzzy c-means algorithm. Comparing to the estimator based on back propagation neural network (BPNN) with momentum method, the novel estimator increases the clustering of NN by boosting learning rates. Simulation results show the fractional slot with q<1 structure effectively depresses cogging force in PMLSM. Experiments prove that the estimator has high accuracy and efficiency. The novel estimator achieves demand of agility design and gives reference for structural parameters selection in PMLSM.
KeywordsRelative Permeability Radial Basis Function Neural Network Back Propagation Neural Network Slot Width Polar Distance
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