Fault-Tolerant Control of a Six-Phase Motor Drive System Using a Takagi-Sugeno-Kang Type Fuzzy Neural Network with Asymmetric Membership Function

IEEE Transactions on Power Electronics

Volumn 28, Issue 7, 2013, Pages 3557-3572

  Lin, Faa Jeng ^a   Hung, Ying Chih ^a   Hwang, Jonq Chin ^b   Tsai, Meng Ting ^a  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Asymmetric membership function (AMF); fault tolerant control; six phase permanent magnet synchronous motor (PMSM); Takagi Sugeno Kang type fuzzy neural network (TSKFNN)

Indexed keywords

ADAPTATION LAW; ASYMMETRIC MEMBERSHIP FUNCTION (AMF); CONTROL PERFORMANCE; CONVERGENCE ANALYSIS; DECISION METHOD; DRIVE SYSTEMS; FAULT TOLERANT CONTROL; FAULT TOLERANT CONTROL SYSTEMS; FAULTY CONDITION; FUZZY LOGIC SYSTEM; NETWORK STRUCTURES; ONLINE LEARNING ALGORITHMS; PERMANENT MAGNET SYNCHRONOUS MOTOR; PMSM DRIVES; SIX-PHASE; SIX-PHASE MOTOR; TAKAGI-SUGENO-KANG TYPE FUZZY NEURAL NETWORK (TSKFNN);

FUZZY NEURAL NETWORKS; LEARNING ALGORITHMS; SIGNAL PROCESSING; SYNCHRONOUS MOTORS;

PERMANENT MAGNETS;

EID: 84872025678     PISSN: 08858993     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPEL.2012.2224888     Document Type: Article

References (34)

1. J. C. Salmon and B. W.Williams, "A split-wound induction motor design to improve the reliability ofPWMinverter drives," IEEE Trans. Ind. Appl., vol. 26, no. 1, pp. 143-150, Jan./Feb. 1990.
2. R. O. C. Lyra and T. A. Lipo, "Torque density improvement in a six-phase induction motor with third harmonic current injection," IEEE Trans. Ind. Appl., vol. 38, no. 5, pp. 1351-1360, Sep./Oct. 2002. (Pubitemid 35303250)
3. H. Zhang, A. Jouanne, S. Dai, K. Wallace, and F. Wang, "Multilevel inverter modulation schemes to eliminate common-mode voltages," IEEE Trans. Ind. Appl., vol. 36, no. 6, pp. 1645-1653, Nov./Dec. 2000. (Pubitemid 32089261)
4. S. Green, D. J. Atkinson, A. G. Jack, B. C. Mecrow, and A. King, "Sensorless operation of a fault tolerant PM drive," IEE Proc. Electr. Power Appl., vol. 150, no. 2, pp. 117-125, Mar. 2003.
5. B. Stumberger, G. Stumberger, A. Hamler, M. Trlep, M. Jesenik, and V. Gorican, "Increasing of output power capability in a six-phase fluxweakened permanent magnet synchronous motor with a third harmonic current injection," IEEE Trans.Magn., vol. 39, no. 5, pp. 3343-3345, Sep. 2003.
6. Y. Izumikawa, K. Yubai, and J. Hirai, "Fault-tolerant control system of flexible arm for sensor fault by using reaction force observer," IEEE/ASME Trans. Mechatron., vol. 10, no. 4, pp. 391-396, Aug. 2005. (Pubitemid 41489319)
7. R. Kianinezhad, B. Nahid-Mobarakeh, L. Baghli, F. Betin, and G. Capolino, "Modeling and control of six-phase symmetrical induction machine under fault condition due to open phases," IEEE Trans. Ind. Appl., vol. 55, no. 5, pp. 1966-1977, May 2008. (Pubitemid 351728751)
8. M. A. Fnaiech, F. Betin, G. Capolino, and F. Fnaiech, "Fuzzy logic and sliding-mode controls applied to six-phase induction machine with open phases," IEEE Trans. Ind. Appl., vol. 57, no. 1, pp. 354-364, Jan. 2010.
9. M. E. H. Benbouzid, D. Diallo, and M. Zeraoulia, "Advanced faulttolerant control of induction-motor drives for ev/hev traction applications: From conventional to modern and intelligent control techniques," IEEE Trans. Veh. Technol., vol. 56, no. 2, pp. 519-528, Mar. 2007. (Pubitemid 46562925)
10. X. Huang, T. G. Habetler, and R. G. Harley, "Detection of rotor eccentricity faults in a closed-loop drive-connected induction motor using an artificial neural network," IEEE Trans. Power Electron., vol. 22, no. 4, pp. 1552-1559, Jul. 2007. (Pubitemid 47098857)
11. A. Sadeghian, Z. Ye, and B. Wu, "Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks," IEEE Trans. Instrum. Meas., vol. 58, no. 7, pp. 2253-2263, Jul. 2009.
12. W. W. Tan and H. Huo, "A generic neurofuzzy model-based approach for detecting faults in induction motors," IEEE Trans. Ind. Electron., vol. 52, no. 5, pp. 1420-1427, Oct. 2005. (Pubitemid 41489386)
13. J. Quiroga, D. A. Cartes, C. S. Edrington, and L. Liu, "Neural network based fault detection of PMSM stator winding short under load fluctuation," in Proc. Int. Power Electron. Motion Control Conf., Sep. 2008, pp. 793-798.
14. V. N. Ghate and S. V. Dudul, "Cascade neural-network-based fault classifier for three-phase induction motor," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1555-1563, May 2011.
15. M. Seera, P. L. Chee, D. Ishak, and H. Singh, "Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-cart model," IEEE Trans. Neural Netw. Learn. Syst., vol. 23, no. 1, pp. 97-108, Jan. 2012.
16. L. X.Wang, A Course in Fuzzy Systems and Control. Englewood Cliffs, NJ: Prentice-Hall, 1997.
17. S. Cong andY. Liang, "PID-like neural network nonlinear adaptive control for uncertain multivariable motion control systems," IEEE Trans. Ind. Electron., vol. 56, no. 10, pp. 3872-3879, Oct. 2009.
18. T. Orlowska-Kowalska,M. Dybkowski, and K. Szabat, "Adaptive slidingmode neuro-fuzzy control of the two-mass induction motor drive without mechanical sensors," IEEE Trans. Ind. Electron., vol. 57, no. 2, pp. 553-564, Feb. 2010.
19. M. J. Er and C. Deng, "Obstacle avoidance of a mobile robot using hybrid learning approach," IEEE Trans. Ind. Electron., vol. 52, no. 3, pp. 898-905, Jun. 2005. (Pubitemid 40871125)
20. F. J. Lin and P. H. Shen, "Robust fuzzy neural network sliding-mode control for two-axis motion control system," IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1209-1225, Jun. 2006.
21. F. J. Lin, P. H. Shieh, and P. H. Chou, "Robust adaptive backstepping motion control of linear ultrasonic motors using fuzzy neural network," IEEE Trans. Fuzzy Syst., vol. 16, no. 3, pp. 672-692, Jun. 2008.
22. A. Gajate, R. E. Haber, P. I. Vega, and J. R. Alique, "A transductive neurofuzzy controller: Application to a drilling process," IEEE Trans. Neural Netw., vol. 21, no. 7, pp. 1158-1167, Jul. 2010.
23. C. S. Chen, "Supervisory interval type-2 TSK neural fuzzy network control for linear microstepping motor drives with uncertainty observer," IEEE Trans. Power Electron., vol. 26, no. 7, pp. 2049-2064, Jul. 2011.
24. R. J.Wai and L. C. Shih, "Adaptive fuzzy-neural-network design for voltage tracking control of a dc-dc boost converter," IEEE Trans. Power Electron., vol. 27, no. 4, pp. 2104-2115, Apr. 2012.
25. C. L. Tseng, S. Y. Wang, S. C. Chien, and C. Y. Chang, "Development of a self-tuning TSK-fuzzy speed control strategy for switched reluctance motor," IEEE Trans. Power Electron., vol. 27, no. 4, pp. 2141-2152, Apr. 2012.
26. F. J. Lin, M. S. Huang, P. Y. Yeh, H. C. Tsai, and C. H. Kuan, "Dsp-based probabilistic fuzzy neural network control for li-ion battery charger," IEEE Trans. Power Electron., vol. 27, no. 8, pp. 3782-3794, Aug. 2012.
27. H. Y. Pan, C. H. Lee, F. K. Chang, and S. K. Chang, "Construction of asymmetric type 2 fuzzy membership function and application in time series prediction," in Proc. Int. Conf. Mach. Learn. Cybern., Aug. 2007, pp. 2024-2030.
28. K. H. Cheng, C. F. Hsu, C. M. Lin, T. T. Lee, andC. Li, "Fuzzy-neural sliding mode control for dc-dc converters using asymmetric Gaussian membership functions," IEEE Trans. Ind. Electron., vol. 54, no. 3, pp. 1528-1536, Jun. 2007.
29. C. H. Lee, T. W. Hu, C. T. Lee, and Y. C. Lee, "A recurrent interval type-2 fuzzy neural network with asymmetric membership functions for nonlinear system identification," in Proc. IEEE Conf. Fuzzy Syst., Jun. 2008, pp. 1496-1502.
30. I. Boldea and S. A. Nasar, Linear Electric Actuators and Generators. London, U.K.: Cambridge Univ. Press, 1997.
31. P. S. Sastry, G. Santharam, and K. P. Unnikrishnan, "Memory neuron networks for identification and control of dynamical systems," IEEE Trans. Neural Netw., vol. 5, no. 2, pp. 306-319, Mar. 1994.
32. F. J. Lin and Y. C. Hung, "FPGA-based elman neural network control system for linear ultrasonic motor," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 56, no. 1, pp. 101-113, Jan. 2009.
33. S. J.Yoo,Y. H. Choi, and J. B. Park, "Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking ofmobile robots: Adaptive learning rates approach," IEEE Trans. Circuits Syst. I: Reg. Papers, vol. 53, no. 6, pp. 1381-1395, Jun. 2006.
34. R. J. Wai and C. M. Li, "Design of dynamic petri recurrent fuzzy neural network and its application to path-tracking control of nonholonomic mobile robot," IEEE Trans. Ind. Electron., vol. 56, no. 7, pp. 2667-2683, Jul. 2009.