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Mathematical Problems in Engineering
Volume 2013, Article ID 715094, 9 pages
http://dx.doi.org/10.1155/2013/715094
Research Article

Particle Swarm Based Approach of a Real-Time Discrete Neural Identifier for Linear Induction Motors

CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. Las Aguilas, Zapopan, Jalisco, Mexico

Received 3 July 2013; Accepted 1 December 2013

Academic Editor: Jun-Juh Yan

Copyright © 2013 Alma Y. Alanis 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. I. Boldea and S. A. Nasar, Linear Electric Actuators and Generators, Cambridge University Press, Cambridge, Mass, USA, 1997.
  2. J. F. Gieras, Linear Inductions Drives, Oxford University Press, Oxford, UK, 1994.
  3. I. Takahashi and Y. Ide, “Decoupling control of thrust and attractive force of a LIM using a space vector control inverter,” IEEE Transactions on Industry Applications, vol. 29, no. 1, pp. 161–167, 1993. View at Publisher · View at Google Scholar · View at Scopus
  4. W. Yu, “Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms,” Information Sciences, vol. 158, no. 1–4, pp. 131–147, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. J. A. Farrell and M. M. Polycarpou, Adaptive ApproxiMation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approxi-Mation Approaches, John Wiley and Sons, New York, NY, USA, 2006.
  6. S. Haykin, Kalman Filtering and Neural Networks, John Wiley and Sons, New York, NY, USA, 2001.
  7. A. Y. Alanis, E. N. Sanchez, and A. G. Loukianov, “Discrete-time adaptive backstepping nonlinear control via high-order neural networks,” IEEE Transactions on Neural Networks, vol. 18, no. 4, pp. 1185–1195, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. R. Kiran, S. R. Jetti, and G. K. Venayagamoorthy, “Online training of a generalized neuron with particle swarm optimization,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '06), pp. 5088–5095, Vancouver, Canada, July 2006. View at Scopus
  9. A. S. Poznyak, E. N. Sanchez, and W. Yu, Differential Neural Networks For Robust Nonlinear Control, World Scientific, Singapore, 2001.
  10. L. J. Ricalde and E. N. Sanchez, “Inverse optimal nonlinear recurrent high order neural observer,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '05), pp. 361–365, Montreal, Canada, August 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. R. Chandra, M. Frean, and M. Zhang, “Adapting modularity during learning in cooperative co-evolutionary recurrent neural networks,” Soft Computing, vol. 16, no. 6, pp. 1009–1020, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Richert, K. Masaud, and C. J. B. Macnab, “Discrete-time weight updates in neural-adaptive control,” Soft Computing, vol. 17, no. 3, pp. 431–444, 2013. View at Publisher · View at Google Scholar
  13. G. A. Rovithakis and M. A. Chistodoulou, Adaptive Control With Recurrent High-Order Neural Networks, Springer, New York, NY, USA, 2000.
  14. S. S. Ge, J. Zhang, and T. H. Lee, “Adaptive neural network control for a class of MIMO nonlinear systems with disturbances in discrete-time,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 34, no. 4, pp. 1630–1645, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Grover and P. Y. C. Hwang, Introduction To Random Signals and Applied Kalman Filtering, John Wiley and Sons, New York, NY, USA, 2nd edition, 1992.
  16. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995. View at Scopus
  17. W. Deng, R. Chen, B. He, Y. Liu, L. Yin, and J. Guo, “A novel two-stage hybrid swarm intelligence optimization algorithm and application,” Soft Computing, vol. 16, no. 10, pp. 1707–1722, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Liu and G. Pender, “Automatic calibration of a rapid flood spreading model using multiobjective optimisations,” Soft Computing, vol. 17, no. 4, pp. 713–724, 2013. View at Publisher · View at Google Scholar
  19. R. L. Welch, S. M. Ruffing, and G. K. Venayagamoorthy, “Comparison of feedforward and feedback neural network architectures for short term wind speed prediction,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '09), pp. 3335–3340, Atlanta, Ga, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. S.-H. Zahiri and S.-A. Seyedin, “Swarm intelligence based classifiers,” Journal of the Franklin Institute, vol. 344, no. 5, pp. 362–376, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Shi and R. Eberhart, “Modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '98), pp. 69–73, May 1998. View at Scopus
  22. M. Clerc, “The swarm and the queen: towards a deterministic and adaptive particle swarm optimization,” Proceedings of the Congress on Evolutionary Computation, vol. 1, pp. 1951–1957.
  23. B. Al-kazemi and C. K. Mohan, “Multi-phase generalization of the particle swarm optimization algorithm,” Proceedings of the Congress on Evolutionary Computation, vol. 1, pp. 489–494, 2002. View at Google Scholar
  24. A. Y. Alanis, E. N. Sanchez, A. G. Loukianov, and M. A. Perez-Cisneros, “Real-time discrete neural block control using sliding modes for electric induction motors,” IEEE Transactions on Control Systems Technology, vol. 18, no. 1, pp. 11–21, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. E. B. Kosmatopoulos, M. M. Polycarpou, M. A. Christodoulou, and P. A. Ioannou, “High-order neural network structures for identification of dynamical systems,” IEEE Transactions on Neural Networks, vol. 6, no. 2, pp. 422–431, 1995. View at Publisher · View at Google Scholar · View at Scopus
  26. V. H. Benitez, A. G. Loukianov, and E. N. Sanchez, “Neural identification and control of a linear induction motor using an α=β model,” in Proceedings of the American Control Conference, pp. 4041–4046, June 2003. View at Scopus
  27. N. Kazantzis and C. Kravaris, “Time-discretization of nonlinear control systems via Taylor methods,” Computers and Chemical Engineering, vol. 23, no. 6, pp. 763–784, 1999. View at Publisher · View at Google Scholar · View at Scopus
  28. A. G. Loukianov, J. Rivera, and J. M. Cañedo, “Discrete time sliding mode control of an induction motor,” in Proceedings IFAC, Barcelone, Spain, 2002.
  29. M. Hernandez-Gonzalez, E. N. Sanchez, and A. G. Loukianov, “Discrete-time neural network control for a linear induction motor,” in Proceedings of the IEEE International Symposium on Intelligent Control (ISIC '08), pp. 1314–1319, September 2008. View at Publisher · View at Google Scholar · View at Scopus