Sensorless Commutation Error Compensation of High Speed Brushless DC Motor Based on RBF Neural Network Method

Author(s):  
Xi Chen ◽  
Haitao Li ◽  
Maolin Sun ◽  
Gang Liu
2010 ◽  
Vol 154-155 ◽  
pp. 1305-1309 ◽  
Author(s):  
Liang Yu Cui ◽  
Da Wei Zhang ◽  
Wei Guo Gao ◽  
Xiang Yang Qi ◽  
Yu Shen

Thermal errors of motorized spindle are of great importance to affect final machining precision of CNC machine tool. Thermal characteristics simulation analysis of motorized spindle is realized by ANSYS; thermal errors test measurement is completed based on 5-point method; and prediction models of thermal errors are constructed by multiple linear regression (MLR) method, Back Propagation (BP) neural network method and Radial Basis Function (RBF) neural network method respectively. The results of simulation and experiments illustrate that simulation results can represent thermal characteristics of motorized spindle, whose degree of confidence mainly depending on setting of thermal load and boundary conditions properly or not; RBF neural network model has highest prediction precision for thermal errors of motorized spindle based on test data.


2011 ◽  
Vol 179-180 ◽  
pp. 707-712
Author(s):  
Hua Ji ◽  
Zhen Yun Han ◽  
Hong Li Liu

With the widely use of Permanent Magnet Brushless DC Motor (BLDCM) in many fields, the superiority in the control method of position sensor-less is more and more obvious. In this paper several control methods of position sensor-less for BLDCM are presents, and a novel technique, the method of speed-independent position function, is introduced to detect the rotor position of BLDCM. This method may detect the rotor position from almost zero speed to high speed, and may also give the commutation time.


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