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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 8760351, 13 pages
https://doi.org/10.1155/2017/8760351
Research Article

Brain Emotional Learning Based Intelligent Decoupler for Nonlinear Multi-Input Multi-Output Distillation Columns

1Electronics, Communications, and Computers Department, Faculty of Engineering, Helwan University, Cairo, Egypt
2Electronics and Communications Department, Pyramids High Institute (PHI) for Engineering and Technology, 6th of October, Giza, Egypt

Correspondence should be addressed to M. H. El-Saify

Received 27 August 2016; Revised 24 December 2016; Accepted 9 January 2017; Published 31 January 2017

Academic Editor: Asier Ibeas

Copyright © 2017 M. H. El-Saify 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. Makaremi and B. Labibi, “Control of a distillation column: a decentralized approach,” in Proceedings of the IEEE International Conference on Control Applications (CCA '06), pp. 711–714, Munich, Germany, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. A. M. El-Garhy and M. E. El-Shimy, “Development of decoupling scheme for high order MIMO process based on PSO technique,” Applied Intelligence, vol. 26, no. 3, pp. 217–229, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  3. H. T. Dorrah, A. M. El-Garhy, and M. E. El-Shimy, “PSO-BELBIC scheme for two-coupled distillation column process,” Journal of Advanced Research, vol. 2, no. 1, pp. 73–83, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. F. Hassan, “Rough sets for adapting wavelet neural networks as a new classifier system,” Applied Intelligence, vol. 35, no. 2, pp. 260–268, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. M. Chen, S. S. Ge, and B. V. E. How, “Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities,” IEEE Transactions on Neural Networks, vol. 21, no. 5, pp. 796–812, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. A. M. El-Garhy, G. A. El-Sheikh, and M. H. El-Saify, “Fuzzy life-extending control of anti-lock braking system,” Ain Shams Engineering Journal, vol. 4, no. 4, pp. 735–751, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. Y.-J. Liu, S. Tong, and C. L. P. Chen, “Adaptive fuzzy control via observer design for uncertain nonlinear systems with unmodeled dynamics,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 2, pp. 275–288, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. F. H. Pereira, W. A. L. Alves, L. Koleff, and S. I. Nabeta, “A two-level genetic algorithm for large optimization problems,” IEEE Transactions on Magnetics, vol. 50, no. 2, pp. 733–736, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. K. Shi and L. Li, “High performance genetic algorithm based text clustering using parts of speech and outlier elimination,” Applied Intelligence, vol. 38, no. 4, pp. 511–519, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Balkenius and J. Morén, “Emotional learning: a computational model of the amygdala,” Cybernetics and Systems, vol. 32, no. 6, pp. 611–636, 2001. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Moren, Emotion and Learning—A Computational Model of the Amygdala [Ph.D. thesis], Lund University, Lund, Sweden, 2002.
  12. C. Lucas, D. Shahmirzadi, and N. Sheikholeslami, “Introducing BELBIC: brain emotional learning based intelligent controller,” Intelligent Automation and Soft Computing, vol. 10, no. 1, pp. 11–22, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Jafarzadeh, M. S. Fadali, and C. Lascu, “An emotional learning intelligent direct torque and flux controller design for induction motor,” in Proceedings of the 4th Annual IEEE Energy Conversion Congress and Exposition (ECCE '12), pp. 3988–3992, Raleigh, NC, USA, September 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. M. A. Rahman, M. A. Milasi, C. Lucas, B. N. Araabi, and T. S. Radwan, “Implementation of emotional controller for interior permanent-magnet synchronous motor drive,” IEEE Transactions on Industry Applications, vol. 44, no. 5, pp. 1466–1476, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Yang, Y. Cao, and L. Zhang, “Design of brain emotional learning model based hydraulic servo system,” in Proceedings of the 2nd International Conference on Mechanic Automation and Control Engineering (MACE '11), pp. 4874–4876, chn, July 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Jafari, A. M. Shahri, and S. B. Shuraki, “Speed control of a digital servo system using brain emotional learning based intelligent controller,” in Proceedings of the 4th Annual International Power Electronics, Drive Systems and Technologies Conference (PEDSTC '13), pp. 311–314, February 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. M. A. Sharbafi, C. Lucas, and R. Daneshvar, “Motion control of omni-directional three-wheel robots by brain-emotional-learning-based intelligent controller,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 40, no. 6, pp. 630–638, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Jafarzadeh, R. Mirheidari, M. R. J. Motlagh, and M. Barkhordari, “Designing PID and BELBIC controllers in path tracking and collision problem in automated highway systems,” in Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision (ICARCV '08), pp. 1562–1566, Hanoi, Vietnam, December 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. E. Bijami, R. Abshari, S. M. Saghaiannejad, and J. Askari, “Load frequency control of interconnected power system using brain emotional learning based intelligent controller,” in Proceedings of the 19th Iranian Conference on Electrical Engineering (ICEE '11), IEEE, Tehran, Iran, May 2011. View at Scopus
  20. S. A. Aghaee, C. Lucas, and K. Amiri Zadeh, “Applying brain emotional learning based intelligent controller (BELBIC) to multiple-area power systems,” Asian Journal of Control, vol. 14, no. 6, pp. 1580–1588, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. E. Lotfi and M. R. Akbarzadeh-T, “Emotional brain-inspired adaptive fuzzy decayed learning for online prediction problems,” in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE '13), Hyderabad, India, July 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. H. Rouhani, M. Jalili, B. N. Araabi, W. Eppler, and C. Lucas, “Brain emotional learning based intelligent controller applied to neurofuzzy model of micro-heat exchanger,” Expert Systems with Applications, vol. 32, no. 3, pp. 911–918, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, Australia, December 1995. View at Scopus
  24. R. C. Eberhart, Y. Shi, and J. Kennedy, Swarm Intelligence (The Morgan Kaufmann Series in Evolutionary Computation), Morgan Kaufmann, San Francisco, Calif, USA, 1st edition, 2001.
  25. X. Liu and J. Qian, “Modeling, control, and optimization of ideal internal thermally coupled distillation columns,” Chemical Engineering and Technology, vol. 23, no. 3, pp. 235–241, 2000. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Miccio and B. Cosenza, “Control of a distillation column by type-2 and type-1 fuzzy logic PID controllers,” Journal of Process Control, vol. 24, no. 5, pp. 475–484, 2014. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Diaz, J. R. Perez-Correa, A. Cipriano, and M. Fernandez-Fernandez, “Intelligent control applications on a binary distillation column,” in Proceedings of the IEEE International Conference on Automatica (ICA-ACCA '16), pp. 1–8, IEEE, October 2016. View at Publisher · View at Google Scholar
  28. Z. Chen, M. A. Henson, P. Belanger, and L. Megan, “Nonlinear model predictive control of high purity distillation columns for cryogenic air separation,” IEEE Transactions on Control Systems Technology, vol. 18, no. 4, pp. 811–821, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. B. Yan, S. X.-D. Tan, L. Zhou, J. Chen, and R. Shen, “Decentralized and passive model order reduction of linear networks with massive ports,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 20, no. 5, pp. 865–877, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. E. Kurniawan, Z. Cao, and Z. Man, “Design of decentralized repetitive control of linear MIMO system,” in Proceedings of the IEEE 8th Conference on Industrial Electronics and Applications (ICIEA '13), pp. 427–432, Melbourne, Australia, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  31. S. Kunimatsu, M. Ishitobi, and T. Fujii, “Decentralized PID control for systems with relative degree no more than 2,” in Proceedings of the 51st Annual Conference on of the Society of Instrument and Control Engineers of Japan (SICE '12), pp. 1106–1109, Akita, Japan, August 2012. View at Scopus
  32. W. Lee, D. Yoon, J. Lee, S. Kim, and S. K. Park, “Decoupling matrix with rate one quasi-orthogonal STBC for eight transmit antennas,” in Proceedings of the 2008 11th IEEE Singapore International Conference on Communication Systems (ICCS '08), pp. 73–76, November 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. M. V. Pavan Kumar and N. Kaistha, “Decentralized control of a kinetically controlled ideal reactive distillation column,” Chemical Engineering Science, vol. 63, no. 1, pp. 228–243, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. C. Rajapandiyan and M. Chidambaram, “Controller design for MIMO processes based on simple decoupled equivalent transfer functions and simplified decoupler,” Industrial and Engineering Chemistry Research, vol. 51, no. 38, pp. 12398–12410, 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. Z. Beheshti and S. Z. M. Hashim, “A review of emotional learning and it's utilization in control engineering,” International Journal of Advances in Soft Computing and its Applications, vol. 2, no. 2, pp. 191–208, 2010. View at Google Scholar · View at Scopus
  36. M. Jalili-Kharaajoo, “Nonlinear system identification using ANFIS based on emotional learning,” in Advances in Artificial Intelligence—IBERAMIA 2004: 9th Ibero-American Conference on AI, Puebla, Mexico, November 22–26, 2004. Proceedings, vol. 3315 of Lecture Notes in Computer Science, pp. 697–707, Springer, Berlin, Germany, 2004. View at Publisher · View at Google Scholar
  37. A. Gholipour, C. Lucas, and D. Shahmirzadi, “Purposeful prediction of space weather phenomena by simulated emotional learning,” International Journal of Modelling and Simulation, vol. 24, no. 2, pp. 65–72, 2004. View at Google Scholar · View at Scopus
  38. M. Parsapoor and U. Bilstrup, “Brain Emotional Learning Based Fuzzy Inference System (BELFIS) for solar activity forecasting,” in Proceedings of the IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI '12), pp. 532–539, Athens, Greece, November 2012. View at Publisher · View at Google Scholar · View at Scopus
  39. J. Abdi, B. Moshiri, B. Abdulhai, and A. K. Sedigh, “Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm,” Engineering Applications of Artificial Intelligence, vol. 25, no. 5, pp. 1022–1042, 2012. View at Publisher · View at Google Scholar · View at Scopus
  40. E. Lotfi, S. Setayeshi, and S. Taimory, “A neural basis computational model of emotional brain for online visual object recognition,” Applied Artificial Intelligence, vol. 28, no. 8, pp. 814–834, 2014. View at Publisher · View at Google Scholar · View at Scopus
  41. N. Abdelkarim, A. E. Mohamed, A. M. El-Garhy, and H. T. Dorrah, “A New Hybrid BFOA-PSO optimization technique for decoupling and robust control of two-coupled distillation column process,” Computational Intelligence and Neuroscience, vol. 2016, Article ID 8985425, 17 pages, 2016. View at Publisher · View at Google Scholar