About this Journal Submit a Manuscript Table of Contents
Abstract and Applied Analysis
Volume 2012 (2012), Article ID 471281, 18 pages
http://dx.doi.org/10.1155/2012/471281
Research Article

Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone

1Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Bulevar Marcelino García Barragán No. 1421, 44430 Guadalajara, JAL, Mexico
2Sección de Estudios de Posgrado e Investigación, ESIME UA-IPN, Avenida de las Granjas No. 682, Colonia Santa Catarina, 02250 Mexico City, DF, Mexico

Received 31 August 2012; Accepted 9 November 2012

Academic Editor: Wenchang Sun

Copyright © 2012 J. Humberto Pérez-Cruz 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. K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4–27, 1990. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Haykin, Neural Networks: A Comprehensive Foundation, IEEE Press, New York, USA, 1994.
  3. F. L. Lewis, S. Jagannathan, and A. Yesildirek, Neural Network Control of Robot Manipulators and Nonlinear Systems, Taylor & Francis, 1999.
  4. G. A. Rovithakis and M. A. Christodoulou, Adaptive Control with Recurrent High-Order Neural Networks: Theory and Industrial Applications, Springer, 2000.
  5. M. Norgaard, O. Ravn, N. K. Poulsen, and L. K. Hansen, Neural Networks for Modelling and Control of Dynamic Systems, Springer, 2000.
  6. A. S. Poznyak, E. N. Sanchez, and W. Yu, Dynamic Neural Networks for Nonlinear Control: Identification, State Estimation and Trajectory Tracking, World Scientific, 2001.
  7. J. Sarangapani, Neural Network Control of Nonlinear Discrete-Time Systems, CRC Press, Taylor & Francis Group, Boca Raton, Fla, USA, 2006.
  8. J. de Jesús Rubio and W. Yu, “Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm,” Neurocomputing, vol. 70, no. 13–15, pp. 2460–2466, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Yu and A. Poznyak, “Indirect adaptive control via parallel dynamic neural networks,” IEE Proceedings: Control Theory and Applications, vol. 146, no. 1, pp. 25–30, 1999.
  10. G. A. Rovithakis, “Tracking control of multi-input affine nonlinear dynamical systems with unknown nonlinearities using dynamical neural networks,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 29, no. 2, pp. 179–189, 1999. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Humberto Pérez-Cruz and A. Poznyak, “Control of nuclear research reactors based on a generalized Hopfield neural network,” Intelligent Automation and Soft Computing, vol. 16, no. 1, pp. 39–60, 2010. View at Scopus
  12. J. H. Perez-Cruz, I. Chairez, A. Poznyak, and J. J. de Rubio, “Constrained neural control for the adaptive tracking of power profiles in a triga reactor,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 7, pp. 4575–4788, 2011. View at Scopus
  13. J. H. Pérez-Cruz, A. Y. Alanis, J. J. Rubio, and J. Pacheco, “System identification using multilayer differential neural networks: a new result,” Journal of Applied Mathematics, vol. 2012, Article ID 529176, 20 pages, 2012. View at Publisher · View at Google Scholar
  14. B. Magyar, C. Hős, and G. Stépán, “Influence of control valve delay and dead zone on the stability of a simple hydraulic positioning system,” Mathematical Problems in Engineering, vol. 2010, Article ID 349489, 15 pages, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  15. A. C. Valdiero, C. S. Ritter, C. F. Rios, and M. Rafikov, “Nonlinear mathematical modeling in pneumatic servo position applications,” Mathematical Problems in Engineering, vol. 2011, Article ID 472903, 16 pages, 2011. View at Publisher · View at Google Scholar
  16. G. Tao and P. V. Kokotović, “Adaptive control of plants with unknown dead-zones,” IEEE Transactions on Automatic Control, vol. 39, no. 1, pp. 59–68, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  17. G. Tao and P. V. Kokotović, Adaptive Control of Systems with Actuator and Sensor Nonlinearities, John Wiley & Sons, New York, NY, USA, 1996.
  18. G. Tao and F. L. Lewis, Adaptive Control of Nonsmooth Dynamic Systems, Springer, 2003.
  19. Y.-J. Sun, “Composite tracking control for generalized practical synchronization of Duffing-Holmes systems with parameter mismatching, unknown external excitation, plant uncertainties, and uncertain deadzone nonlinearities,” Abstract and Applied Analysis, vol. 2012, Article ID 640568, 11 pages, 2012. View at Zentralblatt MATH
  20. H. Cho and E.-W. Bai, “Convergence results for an adaptive dead zone inverse,” International Journal of Adaptive Control and Signal Processing, vol. 12, no. 5, pp. 451–466, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  21. X. S. Wang, H. Hong, and C. Y. Su, “Model reference adaptive control of continuous-time systems with an unknown input dead-zone,” IEE Proceedings: Control Theory and Applications, vol. 150, no. 3, pp. 261–266, 2003. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Zhou and X. Z. Shen, “Robust adaptive control of nonlinear uncertain plants with unknown dead-zone,” IET Control Theory & Applications, vol. 1, no. 1, pp. 25–32, 2007. View at Publisher · View at Google Scholar
  23. X.-S. Wang, C.-Y. Su, and H. Hong, “Robust adaptive control of a class of nonlinear systems with unknown dead-zone,” Automatica, vol. 40, no. 3, pp. 407–413, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  24. Z. Wang, Y. Zhang, and H. Fang, “Neural adaptive control for a class of nonlinear systems with unknown deadzone,” Neural Computing and Applications, vol. 17, no. 4, pp. 339–345, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. Y. J. Liu and N. Zhou, “Observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems with unknown dead-zone input,” ISA Transactions, vol. 49, no. 4, pp. 462–469, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. J. H. Pérez-Cruz, E. Ruiz-Velázquez, J. J. Rubio, and C. A. Alba-Padilla, “Robust adaptive neurocontrol of SISO nonlinear systems preceded by unknown deadzone,” Mathematical Problems in Engineering, vol. 2012, Article ID 342739, 23 pages, 2012. View at Publisher · View at Google Scholar
  27. R. R. Selmic and F. L. Lewis, “Deadzone compensation in motion control systems using neural networks,” IEEE Transactions on Automatic Control, vol. 45, no. 4, pp. 602–613, 2000.
  28. T. P. Zhang and S. S. Ge, “Robust adaptive neural control of SISO nonlinear systems with unknown nonlinear dead-zone and gain sign,” in Proceedings of the IEEE International Symposium on Intelligent Control, pp. 315–320, Munich, Germany, October 2006.
  29. F. L. Lewis, J. Campos, and R. Selmic, Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities, vol. 24, SIAM, Philadelphia, Pa, USA, 2002. View at Publisher · View at Google Scholar