Table of Contents
Advances in Artificial Neural Systems
Volume 2014, Article ID 126317, 8 pages
http://dx.doi.org/10.1155/2014/126317
Research Article

Optimal Design of PID Controller for the Speed Control of DC Motor by Using Metaheuristic Techniques

1University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi 46000, Pakistan
2Faculty of Engineering and Technology, HUIC, Islamabad 44000, Pakistan

Received 22 July 2014; Accepted 19 November 2014; Published 10 December 2014

Academic Editor: Paolo Gastaldo

Copyright © 2014 Mirza Muhammad Sabir and Junaid Ali Khan. 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. W. Cui, Y. Gong, and M. H. Xu, “A permanent magnet brushless DC motor with bifilar winding for automotive engine cooling application,” IEEE Transactions on Magnetics, vol. 48, no. 11, pp. 3348–3351, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. A. Sathyan, M. Krishnamurthy, N. Milivojevic, and A. Emadi, “A low-cost digital control scheme for brushless DC motor drives in domestic applications,” in Proceedings of the 2009 IEEE International Electric Machines and Drives Conference, pp. 76–82, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. P. C. Krause, O. Wasynczuk, and S. D. Sudhoff, Analysis of Electric Machinery and Drives, John Wiley & Sons, 2013.
  4. A. Andrzejewski, “The time-minimal and without overshoot speed control of DC motor,” in Proceedings of the International Conference on Computer as a Tool (EUROCON '07), pp. 1792–1799, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Galijašević, Š. Mašić, S. Smaka, A. Akšamović, and D. Balić, “Parameter identification and digital control of speed of a permanent magnet DC motors,” in Proceedings of the 23rd International Symposium on Information, Communication and Automation Technologies, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. F. E. Hoyos, A. Rincón, J. A. Taborda, N. Toro, and F. Angulo, “Adaptive quasi-sliding mode control for permanent magnet DC motor,” Mathematical Problems in Engineering, vol. 2013, Article ID 693685, 12 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Zhang, L. Ge, M. Shi, and Q. Yang, “Research of compound control for DC motor system based on global sliding mode disturbance observer,” Mathematical Problems in Engineering, vol. 2014, Article ID 759147, 7 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. J. Yao, G. Yang, Z. Jiao, and D. Ma, “Adaptive robust motion control of direct-drive DC motors with continuous friction compensation,” Abstract and Applied Analysis, vol. 2013, Article ID 837548, 14 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Namba, T. Yamamoto, and M. Kaneda, “Robust PID controller and its application,” in Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics, pp. 3636–3641, October 1997. View at Scopus
  10. Y. Harrath, B. Chebel-Morello, and N. Zerhouni, “A genetic algorithm and data mining based meta-heuristic for job shop scheduling problem,” in Proceedings of the 2002 IEEE International Conference on Systems, Man and Cybernetics, pp. 280–285, October 2002. View at Scopus
  11. X. Li and M. Yin, “Self-adaptive constrained artificial bee colony for constrained numerical optimization,” Neural Computing and Applications, vol. 24, no. 3-4, pp. 723–734, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. Cui and X. Gao, “Theory and applications of swarm intelligence,” Neural Computing and Applications, vol. 21, no. 2, pp. 205–206, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. R. G. Kanojiya and P. M. Meshram, “Optimal tuning of PI controller for speed control of DC motor drive using particle swarm optimization,” in Proceedings of the International Conference on Advances in Power Conversion and Energy Technologies (APCET '12), August 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Wang, C. Xia, M. Zhang, and D. Liu, “Adaptive speed control for brushless DC motors based on genetic algorithm and RBF neural network,” in Proceedings of the IEEE International Conference on Control and Automation (ICCA '07), pp. 1219–1222, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. H. M. Asifa and S. R. Vaishnav, “Particle swarm optimisation algorithm based PID controller,” in Proceedings of the 3rd International Conference on Emerging Trends in Engineering and Technology (ICETET '10), pp. 628–631, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. C. Cao, X. Guo, and Y. Liu, “Research on ant colony neural network PID controller and application,” in Proceedings of the 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 253–258, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. M. A. Sahib, B. S. Ahmed, and M. Y. Potrus, “Application of combinatorial interaction design for dc servomotor pid controller tuning,” Journal of Control Science and Engineering, vol. 2014, Article ID 576868, 7 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. Y.-C. Luo, Z.-S. Ke, and Y.-P. Kuo, “Sensorless rotor-field oriented controlled inductiion motor drive with particle swarm optimization algorithm speed controller design strategy,” Controller Design Strategy, vol. 2014, Article ID 861462, 13 pages, 2014. View at Publisher · View at Google Scholar
  19. R. H. B. Richard and C. Dorf, Modern Control Systems, Pearson, 11th edition, 2008.
  20. A. N. A. K. Mishra, “Speed control of DC motor using particle swarm optimization technique,” International Journal of Engineering Research and Technology, vol. 2, no. 6, pp. 1643–1649, 2013. View at Google Scholar
  21. S. D. S. N. Sivanandam, Principles of Soft Computing, John Wiley & Sons, New York, NY, USA, 2nd edition, 2013.
  22. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Wash, USA, December 1995. View at Scopus
  23. J. H. Mathews and K. K. Fink, Numerical Methods Using MATLAB, Prentice-Hall, 4th edition, 2004.
  24. Z.-L. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system,” IEEE Transactions on Energy Conversion, vol. 19, no. 2, pp. 384–391, 2004. View at Publisher · View at Google Scholar · View at Scopus
  25. C.-C. Wong, S.-A. Li, and H.-Y. Wang, “Optimal PID controller design for AVR system,” Tamkang Journal of Science and Engineering, vol. 12, no. 3, pp. 259–270, 2009. View at Google Scholar · View at Scopus