Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2017, Article ID 5813192, 11 pages
https://doi.org/10.1155/2017/5813192
Research Article

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

Laboratory of Engineering of Industrial Systems and Renewable Energy, National High School of Engineers of Tunis (ENSIT), 5 Av. Taha Hussein, BP 56-1008, Tunis, Tunisia

Correspondence should be addressed to Adel Taieb; rf.evil@ledabieat

Received 16 April 2017; Revised 18 June 2017; Accepted 22 August 2017; Published 9 October 2017

Academic Editor: Carla Pinto

Copyright © 2017 Adel Taieb 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. Alvarado, D. Limon, D. M. de la Peña et al., “A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark,” Journal of Process Control, vol. 21, no. 5, pp. 800–815, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Liu, W.-H. Chen, and J. Andrews, “Tracking control of small-scale helicopters using explicit nonlinear MPC augmented with disturbance observers,” Control Engineering Practice, vol. 20, no. 3, pp. 258–268, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Hadj Saïd, F. M'Sahli, M. F. Mimouni, and M. Farza, “Adaptive high gain observer based output feedback predictive controller for induction motors,” Computers and Electrical Engineering, vol. 39, no. 2, pp. 151–163, 2013. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Bououden, M. Chadli, S. Filali, and A. El Hajjaji, “Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach,” Renewable Energy, vol. 37, no. 1, pp. 434–439, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. V. Kirubakaran, T. K. Radhakrishnan, and N. Sivakumaran, “Distributed multiparametric model predictive control design for a quadruple tank process,” Measurement: Journal of the International Measurement Confederation, vol. 47, no. 1, pp. 841–854, 2014. View at Publisher · View at Google Scholar · View at Scopus
  6. B. M. Al-Hadithi, A. Jiménez, and J. Perez-Oria, “New incremental Takagi-Sugeno state model for optimal control of multivariable nonlinear time delay systems,” Engineering Applications of Artificial Intelligence, vol. 45, pp. 259–268, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. H. Gouta, S. H. Said, and F. M’Sahli, “Predictive and backstepping control of double tank process: A comparative study,” IETE Technical Review (Institution of Electronics and Telecommunication Engineers, India), vol. 33, no. 2, pp. 137–147, 2016. View at Publisher · View at Google Scholar · View at Scopus
  8. S.-J. Tsai, C.-L. Huo, Y.-K. Yang, and T.-Y. Sun, “Variable feedback gain control design based on particle swarm optimizer for automatic fighter tracking problems,” Applied Soft Computing Journal, vol. 13, no. 1, pp. 58–75, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks (ICNN ’95), vol. 4, pp. 1942–1948, Perth, Western Australia, November-December 1995. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Bououden, M. Chadli, F. Allouani, and S. Filali, “A new approach for fuzzy predictive adaptive controller design using particle swarm optimization algorithm,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 9, pp. 3741–3758, 2013. View at Google Scholar · View at Scopus
  11. A. Taeib, M. Soltani, and A. Chaari, “Model predictive control based on chaos particle swarm optimization for nonlinear processes with constraints,” Kybernetes. The International Journal of Cybernetics, Systems and Management Sciences, vol. 43, no. 9-10, pp. 1469–1482, 2014. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116–132, 1985. View at Google Scholar · View at Scopus
  13. B. Kosko, “Fuzzy systems as universal approximators,” in Proceedings of the 1st IEEE International Conference on Fuzzy Systems (FUZZ '92), pp. 1153–1162, March 1992. View at Scopus
  14. P. Kittisupakorn, P. Thitiyasook, M. A. Hussain, and W. Daosud, “Neural network based model predictive control for a steel pickling process,” Journal of Process Control, vol. 19, no. 4, pp. 579–590, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. A. Steinboeck, D. Wild, and A. Kugi, “Nonlinear model predictive control of a continuous slab reheating furnace,” Control Engineering Practice, vol. 21, no. 4, pp. 495–508, 2013. View at Publisher · View at Google Scholar · View at Scopus
  16. H. Sarimveis and G. Bafas, “Fuzzy model predictive control of non-linear processes using genetic algorithms,” Fuzzy Sets and Systems. An International Journal in Information Science and Engineering, vol. 139, no. 1, pp. 59–80, 2003. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. H. Jiang, C. K. Kwong, Z. Chen, and Y. C. Ysim, “Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control,” Expert Systems with Applications, vol. 39, no. 1, pp. 194–201, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. Y. Li, J. Shen, K. Y. Lee, and X. Liu, “Offset-free fuzzy model predictive control of a boiler-turbine system based on genetic algorithm,” Simulation Modelling Practice and Theory, vol. 26, pp. 77–95, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. J. A. Roubos, R. Babuska, P. M. Bruijn, and H. B. Verbruggen, “Predictive control by local linearization of a Takagi-Sugeno fuzzy model,” in Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 37–42, May 1998. View at Scopus
  20. Y. L. Huang, H. H. Lou, J. P. Gong, and T. F. Edgar, “Fuzzy model predictive control,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 6, pp. 665–678, 2000. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Shi and R. Eberhart, “Parameter selection in particle swarm optimization,” in Evolutionary Programming VII, vol. 1447 of Lecture Notes in Computer Science, pp. 591–600, Springer, New York, NY, USA, 1998. View at Publisher · View at Google Scholar
  22. J. Yu, S. Wang, and L. Xi, “Evolving artificial neural networks using an improved PSO and DPSO,” Neurocomputing, vol. 71, no. 4–6, pp. 1054–1060, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. R. C. Eberhart and Y. Shi, “Particle swarm optimization: developments, applications and resources,” in Proceedings of the Congress on Evolutionary Computation, vol. 1, pp. 81–86, Seoul, Republic of Korea, 2001. View at Publisher · View at Google Scholar
  24. M. Rashid and A. R. Baig, “Psogp: A genetic programming based adaptable evolutionary hybrid particle swarm optimization,” International Journal of Innovative Computing, Information and Control, vol. 6, no. 1, pp. 287–296, 2010. View at Google Scholar · View at Scopus
  25. I. Pan and S. Das, “Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO,” ISA Transactions, vol. 62, pp. 19–29, 2016. View at Publisher · View at Google Scholar · View at Scopus
  26. M. A. Duarte-Mermoud and F. Milla, “Model predictive power stabilizer optimized by PSO,” in Proceedings of the IEEE International Conference on Automatica, ICA-ACCA, October 2016. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Zhao, Y. Yang, and Y. Zeng, “Eliciting compact T-S fuzzy models using subtractive clustering and coevolutionary particle swarm optimization,” Neurocomputing, vol. 72, no. 10-12, pp. 2569–2575, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. P. Angeline, “Evolutionary optimization versus particle swarm optimization: philosophy and performance differences,” in Evolutionary Programming VII, V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eiben, Eds., vol. 1447 of Lecture Notes in Computer Science, pp. 601–610, Springer, Berlin, Germany, 1998. View at Publisher · View at Google Scholar
  29. B. Liu, L. Wang, Y. Jin, F. Tang, and D. Huang, “Improved particle swarm optimization combined with chaos,” Chaos, Solitons and Fractals, vol. 25, no. 5, pp. 1261–1271, 2005. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. Shi and R. Eberhart, “A modifieded particle swarm optimization,” in Proceedings of the IEEE Int. Conf. Evol. Computer, pp. 591–600, Anchorage, Alaska, USA, 1998.
  31. L. D. S. Coelho and V. C. Mariani, “A novel chaotic particle swarm optimization approach using Hénon map and implicit filtering local search for economic load dispatch,” Chaos, Solitons & Fractals, vol. 39, no. 2, pp. 510–518, 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. O. Ait Sahed, K. Kara, and M. L. Hadjili, “Constrained Fuzzy Predictive Control Using Particle Swarm Optimization,” Applied Computational Intelligence and Soft Computing, vol. 2015, pp. 1–15, 2015. View at Publisher · View at Google Scholar
  33. M. Ikravesh, Dynamic neural network control, ph.d. dissertation [Ph.D. thesis], In University of South Carolina, Columbia, SC, USA, 1994.