Hybrid Intelligent Method of Identifying Stator Resistance of Motorized Spindle


Share / Export Citation / Email / Print / Text size:

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.

Content not available PDF Share



[1]Yusheng Zhang, “The Application of Electro-spindle on CNC Machine”, Mechanical Management and Development, vol.1, 2011, pp. 137-138.
[2] L P Wang, H G Zhang, X H Liu, “Robust sensorless of ADRC controlled PMSM based on MRAS with stator resistance identification”, Proc. CCC 2011, pp.3575-3579, China, July 22-24, 2011.
[3] Junling Zhu, Qing Tian, Zuobao Tian, “Simulation analysis on stator resistance identification based on fuzzy logic”, Computer Measurement & control, vol.20,No. 8 , 2012 ,pp.2230-2232.
[4] Kumar R, Gupta R A, Bansal A K, “Identification and control of PMSM using ANN”, Journal of the Institution of Engineering(India):Electrical Engineering Division, vol.90, 2009, pp.20-25.
[5] Foo Gilbert Hock Beng,Rahman M. F., “Direct torque control of an IPM-synchronous motor drive at very low speed using a sliding-mode stator flux observer”, IEEE Transactions on Power Electronics, vol.25,No. 4 , 2010, pp.933-942.
[6] Barut Murat, “Bi input-extended kalman filter based estimation technique for speed-sensorless control of induction motors”, Energy Conversion and Management,vol.51,No. 10, 2010, pp. 2032-2040.
[7] Soltani J., Abootorabi Zarchi H,Arab Markadeh Gh R, “Stator-flux-oriented based encoderless direct torque control for synchronous reluctance machines using sliding mode approach” , World Academy of Science, Engineering and Technology.vol.58 , 2009, pp.883-889.
[8] Dan Xiao, Foo Gilbert,Rahman M. F, “A new combined adaptive flux observer with HF signal injection for sensorless direct torque and flux control of matrix converter fed ipmsm over a wide speed range” , Proc. ECCE 2010,pp 1859-1866, United states, September 12-16,2010.
[9] Foo Gilbert,Rahman M F, “Sensorless direct torque and flux-controlled IPM synchronous motor drive at very low speed without signal injection”,IEEE Transactions on Industrial Electronics, vol.57,No.1,2010 , pp.395-403.
[10] Abjadi Navid R, Markadeh Gholamreza Arab,Soltani Jafar, “Model following sliding-mode control of a six-phase induction motor drive” ,Journal of Power Electronics, vol.10,No. 6,2010, pp. 694-701.
[11] Zidani F, Diallo D, Benbouzid M E H,Naït-Saïd R, “Direct torque control of induction motor with fuzzy stator resistance adaptation” ,IEEE Transactions on Energy Conversion, vol. 21, No. 2, 2006 ,pp.619-621
[12] Zaimeddine Rabah, Berkouk E Madjid,Refoufi Larbi, “Two approaches for direct torque control using a three-level voltage source inverter with real time estimation of an induction motors stator resistance” ,Mediterranean Journal of Measurement and Control, vol.3, No. 3, 2007 , pp.134-142.
[13] Sayouti Yassine, Abbou Ahmed, Akherraz Mohammed,Mahmoudi Hassane, “On-line neural network stator resistance estimation in direct torque controlled induction motor drive” ,Proc. ISDA 2009,pp 988-992, Italy, November 30 - December 2, 2009.
[14] Tlemcani Abdelhalim, Bouchhida Ouahid, Benmansour Khelifa, Boudana Djamell,Boucherit Mohamed Seghir, “Direct torque control strategy (DTC) based on fuzzy logic controller for a permanent magnet synchronous machine drive” ,Journal of Electrical Engineering and Technology, vol. 4, No. 1, 2009, pp.66-78.
[15] Aktas Mustafa,Ibrahim Okumus H, “Stator resistance estimation using ANN in DTC IM drives” , Turkish Journal of Electrical Engineering and Computer Sciences , vol. 18, No. 2, 2010, pp.197-210.
[16] Draou Azeddine,Miloudi Abdellah, “A simplified speed controller for direct torque neuro fuzzy controlled induction machine drive based on a variable gain PI controller” , Proc. PEOCO 2010, pp 533-538, Malaysia, June 23-24, 2010.
[17] Rong Guo, Hong sheng Tang, Ge Zhong xue, et. al, “Application of NNPLS analysis in QSDR studies”, Computers and Applied Chemistry, vol. 26, No. 12, 2009,pp.1553-1558.
[18] Rui hua Li, Guo xiang Meng, Heng kun Xie,etal, “A Hybrid Prediction Approach for Stator Insulation Breakdown Voltage of Large Generator Based on PLS Combined With Artificial Neural Network”, Zhongguo Dianji Gongcheng Xuebao, vol.27, No. 3,2007,pp.100-105.
[19] Abdul Rahim, H. Ibrahim, F. Taib, M.N. “System identification of nonlinear autoregressive models in monitoring dengue infection” ,International Journal on Smart Sensing and Intelligent Systems, vol.3,No.4 ,2010, pp.783-806.
[20] Dey, Debangshu,Munshi, Sugata, “Simulation studies on a new intelligent scheme for relative humidity and temperature measurement using thermistors in 555 timer circuit”, International Journal on Smart Sensing and Intelligent Systems, vol. 3,No. 2, 2010, pp.217-229.
[21] G. Sen Gupta, S.C. Mukhopadhyay, S. Demidenko and C.H. Messom, “Master-slave Control of a Teleoperated Anthropomorphic Robotic Arm with Gripping Force Sensing”, IEEE Transactions on Instrumentation and Measurement, Vol. 55, No. 6, pp. 2136-2145, December 2006.
[22] Xiao qiang Zhu, Guo qiang Li, Ling fang Sun, etal, “Analysis of Carbon in Coal-fired Based on Partial Least Squares Regression Algorithm”, Journal of Northeast Dianli University, vol.32,No.3, 2012, pp.31-35.
[23] S.C. Mukhopadhyay, T.Ohji, M.Iwahara and S.Yamada, "Modeling and Control of a New Horizontal Shaft Hybrid Type Magnetic Bearing", IEEE Transactions on Industrial Electronics, Vol. 47, No. 1, pp. 100-108, February 2000.
[24] N Wang, J Xu, J Yang, “Method of identifying key quality characteristics in multistage manufacturing process based on PLSR”, Proc. ADME 2012, pp.2580-2584, China, August 16-18, 2012.
[25] S.C. Mukhopadhyay, T.Ohji, M.Iwahara and S.Yamada, "Design, Analysis and Control of a New Repulsive Type Magnetic Bearing", IEE proceeding on Electric Power Applications, vol. 146, no. 1, pp. 33-40, January 1999.
[26] Cheng yuan Wang, Jia kuan Xia, Jun youYang, “Modern control technology of motor”, Mechanical industry press, China,2006.