Table of Contents
Advances in Artificial Neural Systems
Volume 2013, Article ID 410870, 7 pages
http://dx.doi.org/10.1155/2013/410870
Research Article

Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent Neural Network

1Mechanical Engineering Group, Aligudarz Branch, Islamic Azad University, P.O. Box 159, Aligudarz, Iran
2Faculty of Engineering, Shahrekord University, P.O. Box 115, Shahrekord, Iran

Received 14 March 2013; Revised 1 June 2013; Accepted 6 June 2013

Academic Editor: Chao-Ton Su

Copyright © 2013 Mohammad Heidari and Hadi Homaei. 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. T. Hägglund, “A friction compensator for pneumatic control valves,” Journal of Process Control, vol. 12, no. 8, pp. 897–904, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. M. A. De Souza L. Cuadros, C. J. Munaro, and S. Munareto, “Improved stiction compensation in pneumatic control valves,” Computers and Chemical Engineering, vol. 38, pp. 106–114, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. R. P. Champagne and S. J. Boyle, “Optimizing valve actuator parameters to enhance control valve performance,” ISA Transactions, vol. 35, no. 3, pp. 217–223, 1996. View at Google Scholar · View at Scopus
  4. H. Paynter, Analysis and Design of Engineering Systems, MIT Press, Cambridge, Mass, USA, 1959.
  5. D. C. Karnopp, R. C. Rosenberg, and D. L. Margolis, System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems, John Wiley & Sons, New York, NY, USA, 5th edition, 2012.
  6. J. U. Thoma, Simulation by Bondgraphs: Introduction to a Graphical Method, Springer, Berlin, Germany, 2012.
  7. P. Athanasatos and T. Costopoulos, “Proactive fault finding in a 4/3-way direction control valve of a high pressure hydraulic system using the bondgraph method with digital simulation,” Mechanism and Machine Theory, vol. 50, pp. 64–89, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. V. D. Zuccarini, D. Rafirou, J. LeFevre, D. R. Hose, and P. V. Lawford, “Systemic modelling and computational physiology: the application of bondgraph boundary conditions for 3D cardiovascular models,” Simulation Modelling Practice and Theory, vol. 17, no. 1, pp. 125–136, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. O. Ekren, S. Sahin, and Y. Isler, “Comparison of different controllers for variable speed compressor and electronic expansion valve,” International Journal of Refrigeration, vol. 33, no. 6, pp. 1161–1168, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. S. B. Choi, C. C. Cheong, J. M. Jung, and Y. T. Choi, “Position control of an er valve-cylinder system via neural network controller,” Mechatronics, vol. 7, no. 1, pp. 37–52, 1997. View at Google Scholar · View at Scopus
  11. J. C. Mackanic, Design, Construction and Evaluation of a Simulated Geothermal Flow System, University of California, Berkeley, Calif, USA, 1980.
  12. K. Ogata, Modern Control Engineering, Prentice Hall, Upper Saddle River, NJ, USA, 5th edition, 2010.
  13. S. Zerkaoui, F. Druaux, E. Leclercq, and D. Lefebvre, “Stable adaptive control with recurrent neural networks for square MIMO non-linear systems,” Engineering Applications of Artificial Intelligence, vol. 22, no. 4-5, pp. 702–717, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Hernandez and Y. Tang, “Adaptive output-feedback decentralized control of a class of second order nonlinear systems using recurrent fuzzy neural networks,” Neurocomputing, vol. 73, no. 1–3, pp. 461–467, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. H. W. Ge, W. L. Du, F. Qian, and Y. C. Liang, “Identification and control of nonlinear systems by a time-delay recurrent neural network,” Neurocomputing, vol. 72, no. 13–15, pp. 2857–2864, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Demuth and M. Beale, Matlab Neural Networks Toolbox, User’s Guide, The MathWorks, Natick, Mass, USA, 2001, http://www.mathworks.com/.
  17. N. S. Nise, Control System Engineering, John Wiley & Sons, New York, NY, USA, 6th edition, 2010.