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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 974974, 13 pages
http://dx.doi.org/10.1155/2013/974974
Research Article

Adaptive Two-Stage Extended Kalman Filter Theory in Application of Sensorless Control for Permanent Magnet Synchronous Motor

1School of Electric Power, South China University of Technology, Guangzhou, Guangdong 510640, China
2Hunan Institute of Humanities Science and Technology, Loudi, Hunan 417000, China

Received 20 June 2013; Revised 26 September 2013; Accepted 17 October 2013

Academic Editor: Shihua Li

Copyright © 2013 Boyu Yi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. B. K. Bose, Power Electronics and Variable Frequency Drives—Technology and Application, IEEE Press, New York, NY, USA, 1997.
  2. X. Yue, D. M. Vilathgamuwa, and K.-J. Tseng, “Observer-based robust adaptive control of PMSM with initial rotor position uncertainty,” IEEE Transactions on Industry Applications, vol. 39, no. 3, pp. 645–656, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. Y.-S. Kung and M.-H. Tsai, “FPGA-based speed control IC for PMSM drive with adaptive fuzzy control,” IEEE Transactions on Power Electronics, vol. 22, no. 6, pp. 2476–2486, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. A.-R. I. Mohamed, “Design and implementation of a robust current-control scheme for a PMSM vector drive with a simple adaptive disturbance observer,” IEEE Transactions on Industrial Electronics, vol. 54, no. 4, pp. 1981–1988, 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. K. Jezernik, J. Korelic, and R. Horvat, “PMSM sliding mode FPGA-based control for torque ripple reduction,” IEEE Transactions on Power Electronics, vol. 28, no. 7, pp. 3549–3556, 2013. View at Publisher · View at Google Scholar
  6. J. Solsona, M. Valla, and C. Muravchik, “Nonlinear control of a permanent magnet synchronous motor with disturbance torque estimation,” IEEE Transactions on Energy Conversion, vol. 15, no. 2, pp. 163–168, 2000. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Li and H. Gu, “Fuzzy adaptive internal model control schemes for PMSM speed-regulation system,” IEEE Transactions on Industrial Informatics, vol. 8, no. 4, pp. 767–779, 2012. View at Publisher · View at Google Scholar
  8. E. Kim and S. Lee, “Output feedback tracking control of MIMO systems using a fuzzy disturbance observer and its application to the speed control of a PM synchronous motor,” IEEE Transactions on Fuzzy Systems, vol. 13, no. 6, pp. 725–741, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. S. Li and Z. Liu, “Adaptive speed control for permanent-magnet synchronous motor system with variations of load inertia,” IEEE Transactions on Industrial Electronics, vol. 56, no. 8, pp. 3050–3059, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Liu and S. Li, “Speed control for PMSM servo system using predictive functional control and extended state observer,” IEEE Transactions on Industrial Electronics, vol. 59, no. 2, pp. 1171–1183, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. V. Q. Leu, H. H. Choi, and J.-W. Jung, “Fuzzy sliding mode speed controller for PM synchronous motors with a load torque observer,” IEEE Transactions on Power Electronics, vol. 27, no. 3, pp. 1530–1539, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. A. K. Abdelsalam, M. I. Masoud, M. S. Hamad, and B. W. Williams, “Improved sensorless operation of a CSI-based induction motor drive: long feeder ase,” IEEE Transactions on Power Electronics, vol. 28, no. 8, pp. 4001–4012, 2013. View at Publisher · View at Google Scholar
  13. G. Wang, Z. Li, G. Zhang, Y. Yu, and D. Xu, “Quadrature PLL-based high-order sliding-mode observer for IPMSM sensorless control with online MTPA control strategy,” IEEE Transactions on Energy Conversion, vol. 28, no. 1, pp. 214–224, 2013. View at Publisher · View at Google Scholar
  14. T. J. Vyncke, R. K. Boel, and J. A. A. Melkebeek, “On the stator flux linkage estimation of an PMSM with extended Kalman filters,” in Proceedings of the 5th IET International Conference on Power Electronics, Machines and Drives (PEMD '10), pp. 19–21, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Foo, S. Sayeef, and M. F. Rahman, “SVM direct torque controlled interior permanent magnet synchronous motor drive using an extended Kalman filter,” in Proceedings of the 4th IET International Conference on Power Electronics, Machines and Drives (PEMD '08), pp. 712–716, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Barut and R. Demir, “Bi input-extended Kalman filter based speed-sensorless direct torque control of IMs,” in Proceedings of the 19th International Conference on Electrical Machines (ICEM '10), pp. 1–5, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. I. M. Alsofyani, N. Idris, T. Sutikno, and Y. A. Alamri, “An optimized Extended Kalman Filter for speed sensorless direct troque control of an induction motor,” in Proceedings of the IEEE International Conference on Power and Energy (PECON '12), pp. 319–324, 2012.
  18. D.-J. Jwo and S.-H. Wang, “Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation,” IEEE Sensors Journal, vol. 7, no. 5, pp. 778–789, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. D. H. Zhou and P. M. Frank, “Strong tracking filtering of nonlinear time-varying stochastic systems with coloured noise: application to parameter estimation and empirical robustness analysis,” International Journal of Control, vol. 65, no. 2, pp. 295–307, 1996. View at Publisher · View at Google Scholar · View at MathSciNet
  20. R. K. Mehra, “On the identification of variances and adaptive Kalman filtering,” IEEE Transactions on Automatic Control, vol. 15, no. 2, pp. 175–184, 1970. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. D. Loebis, R. Sutton, J. Chudley, and W. Naeem, “Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system,” Control Engineering Practice, vol. 12, no. 12, pp. 1531–1539, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. A. Chatterjee and F. Matsuno, “A neuro-fuzzy assisted extended Kalman filter-based approach for simultaneous localization and mapping (SLAM) problems,” IEEE Transactions on Fuzzy Systems, vol. 15, no. 5, pp. 984–997, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Ozbek and M. Efe, “An adaptive Extended Kalman Filter with application to compartment models,” Communications in Statistics B, vol. 33, no. 1, pp. 145–158, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  24. B. Friedland, “Treatment of bias in recursive filtering,” IEEE Transactions on Automatic Control, vol. 14, no. 4, pp. 359–367, 1969. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. M. B. Ignagni, “An alternate derivation and extension of Friedland's two-stage Kalman estimator,” IEEE Transactions on Automatic Control, vol. 26, no. 3, pp. 746–750, 1981. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  26. J. M. Mendel and H. D. Washburn, “Multistage estimation of bias states in linear systems,” International Journal of Control, vol. 28, no. 4, pp. 511–524, 1978. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  27. A. T. Alouani, P. Xia, T. R. Rice, and W. D. Blair, “On the optimality of two-stage state estimation in the presence of random bias,” IEEE Transactions on Automatic Control, vol. 38, no. 8, pp. 1279–1282, 1993. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  28. C.-S. Hsieh and F.-C. Chen, “Optimal solution of the two-stage Kalman estimator,” IEEE Transactions on Automatic Control, vol. 44, no. 1, pp. 194–199, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  29. D. H. Zhou, Y. X. Sun, Y. G. Xi, and Z. J. Zhang, “Extension of Friedland's separate-bias estimation to randomly time-varying bias of nonlinear systems,” IEEE Transactions on Automatic Control, vol. 38, no. 8, pp. 1270–1273, 1993. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  30. C.-S. Hsieh, “General two-stage extended Kalman filters,” IEEE Transactions on Automatic Control, vol. 48, no. 2, pp. 289–293, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  31. A. Shademan and F. J. Sharifi, “Sensitivity analysis of EKF and iterated EKF pose estimation for position-based visual servoing,” in Proceedings of the IEEE Conference on Control Applications (CCA '05), pp. 755–760, 2005.
  32. K. H. Kim, J. G. Lee, and C. G. Park, “Adaptive two-stage extended Kalman filter for a fault-tolerant INS-GPS loosely coupled system,” IEEE Transactions on Aerospace and Electronic Systems, vol. 45, no. 1, pp. 125–137, 2009. View at Publisher · View at Google Scholar · View at Scopus
  33. M. Hilairet, F. Auger, and E. Berthelot, “Speed and rotor flux estimation of induction machines using a two-stage extended Kalman filter,” Automatica, vol. 45, no. 8, pp. 1819–1827, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  34. A. Akrad, M. Hilairet, and D. Diallo, “A sensorless PMSM drive using a two stage extended Kalman estimator,” in Proceedings of the 34th Annual Conference of the IEEE Industrial Electronics Society (IECON '08), pp. 2776–2781, November 2008. View at Publisher · View at Google Scholar · View at Scopus