Hybrid Intelligent Method of Identifying Stator Resistance of Motorized Spindle


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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 7 , ISSUE 2 (June 2014) > List of articles

Hybrid Intelligent Method of Identifying Stator Resistance of Motorized Spindle

Lixiu Zhang * / Yuhou Wu * / Ke Zhang *

Keywords : vector control, stator resistance, neural network, PLS, identification, Motorized Spindle

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 2, Pages 781-797, DOI: https://doi.org/10.21307/ijssis-2017-681

License : (CC BY-NC-ND 4.0)

Received Date : 16-January-2014 / Accepted: 15-April-2014 / Published Online: 27-December-2017



Aiming at the problem that changes of nonlinear dynamic resistance of stator affect the performance of speed sensorless vector control system, a hybrid computing intelligence approach is used in the identification of stator resistance of motorized spindle. The partial least squares (PLS) regression is combined with neural network to solve the problem of few samples and multi-correlation of variables in complicated data modeling. The PLS method is used to extract variable components from sample data and then reduced the dimension of input variables. Moreover, neural network is used to fit the non-linearity between input and output variables. The model based on partial least squares regression and neural network can identify stator resistance under different conditions of the motorized spindle. The results show that the method has high identification precision and is helpful to improve the performance of vector control system.

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