International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Magnetic Train System by Interval Type-2 Fuzzy Logic Control Based on Social Spider Optimization

Abstract

Those attractive suspension frameworks of a low speed magnetic levitation train will be taken similarly as this article about study. The design and analysis of the suspended intelligent console on the magnetic train, and the redesign of the non-linear mathematical equations of the magnetic train suspension model were discussed. The structuring and implementation of an Interval Type-2 fuzzy controller and Type-1 Fuzzy controller like PID Controller to control the Magnetic Train in non-linear attractive levitation demonstrate to pursue the ideal position. Enough results are included to demonstrate that the power controller significantly reduces the parameter blurring effect (uncertainty) by performing a seamless control action. The parameters of IT2FLC-PID tuning by social spider Optimization Method (SSO) to achieve least error .Structuring and implementation of the Interval Type-2 fuzzy controller and Type-1 Fuzzy controller like PID Controllers to control the train in a nonlinear attractive levitation demonstrate to pursue the ideal position. The parameters of PID tuning by social spider Optimization Method (SSO) to achieve least error in the two controllers (IT2FLC and T1FLC). Metal train mass and coil resistance value changes as blurring (uncertainty) working to the magnetic train levitation system. The results with a non-linear magnetic train and blurring (uncertainty) parameters under the MATLAB condition are compared in several tests such as standard, blurring (uncertainty), and robustness.

Country : Iraq

1 Ahmed A. Oglah

  1. Control and Systems Engineering Department, University of Technology, Iraq

IRJIET, Volume 4, Issue 4, April 2020 pp. 25-35

doi.org/10.47001/IRJIET/2020.404004

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References

  1. L. Hyung Woo, K. Ki Chan., L. Ju, “Review of Maglev Train Technologies” IEEE Transactions. Magnetics, V. 42, No. 7, PP. 1917-1925, 2006.
  2. S. You  Gang, L. Wanli, H.  Qiang, “An Experimental Study on the Vibration of the Low-Speed Maglev Train Moving on the Guideway with Sag Vertical Curves,” International Journal Control and Automation, V.9, No. 4, PP. 279-288, April 2016.
  3. D. Richard ,  “Efficient and Affordable Maglev Opportunities in the United States” , Proceeding of the  IEEE, V. 97, No. 11, PP. 1901-1921, November 2009.
  4. Y. Sun, J. Xu,H. Qiang,and G. Lin “Adaptive Neural-Fuzzy Robust Position Control Scheme for Maglev Train Systems with Experimental Verification,” IEEE Transactions on Industrial Electronics, V. 1, No.1, PP. 1-1, January, 2019.
  5. M. Zhou, M. Liu. “Key Technologies in the Construction of Medium and Low Speed Maglev in Changsha City,” Urban Mass, V. 19, No. 5,PP. 1-4, May, 2016.
  6. R. J. Wai, J. D. Lee. “Key Technologies in the Construction of Medium and Low Speed Maglev in Changsha City,” IEEE T. Control  System., V. 17, No.1, PP. 4-14, January, 2009.
  7. D,  Xiaodong, M. Moein, K, Mir. Behrad. “Dual-Axial Motion Control of a Magnetic Levitation System Using Hall-Effect Sensors,” IEEE-ASME Transactions on Mechatronic., V.21, No.2, PP. 1129- 1139, April. 2016.
  8. Z. Long, A. Hao, S. Chang. “Suspension Controller Design of Maglev Train Considering the Rail Track Periodical Irregularity,” Journal of National University of Defense Technology, V. 25, No. 2, PP. 84-89, April 2003.
  9. D. Lindlu, R . Knosp. “Feedback linearization of an active magnetic bearing with voltage control,” IEEE Transactions on Control Systems Technology., Issue 1, No. 1, PP. 21-31, January. 2002.
  10. W.-R. Song,H.-X. Han, H.-Y. Heand G.-F. Yu. “PID control of micro feed mechanism based on magnetic levitation technology,” Journal of Harbin Institute of Technology, V. 36, No. 1, PP. 28-31, January. 2004.
  11. L. Dai,. B. Qi, H. Zhou, et al. “PID control and experiment for magnetism levitation movement system,” Mod. Manuf. Engineering,. V. 6, PP. 79-82, 2008.
  12. H. Wang, X. B. Zhong, G. Shen. “A New Maglev Line System Design and Control Strategy,” Journal of Tongji University, V. 41, No. 7, PP. 1112-1118, July. 2013.
  13. W. Jong, ,C. Meng, Y. Jing,. “Observer-based adaptive fuzzy-neural-network control for hybrid maglev transportation system,” Neurocomputing , V. 175part A, PP. 10-24, January. 2016.
  14. T. Xuan, H. Kang, “Arbitrary Finite-time Tracking Control for Magnetic Levitation Systems,” Int. J. Adanced. Robotics Systems, V. 11, PP. 1-12. , January,. 2014.
  15. W.Hui, Z. Xiaobo, B. Zhong, S. Gang,. “Analysis and experimental study on the MAGLEV vehicle-guide way interaction based on the full-state feedback theory”. Journal of Vibration and Control, V. 12, No. 2, PP. 51-74, February, 2015.
  16. S. Yougang, Q. Haiyan, L. Guobin,R. Jingdong,L. Wanli “Dynamic Modeling and control of nonlinear electromagnetic suspension systems,” Chemical Engineering Transactions, V. 46, PP. 1039-1045, December, 2015.
  17. Q. Haiyan , L. Wanli, S .Yougang . “Levitation chassis dynamic analysis and robust position control for maglev vehicles under nonlinear periodic disturbance,” Journal Vibration Engineering, V. 19, No. 2, PP. 1273-1286, Mars. 2017.
  18. L. Jinhui, L. Jie,Z. Danfeng, et al. “The Active Control of Maglev Stationary Self-Excited Vibration with a Virtual Energy Harvester,” IEEE Transactions on Industrial Electronics, V. 62, No. 5, PP. 2942-2951,May 2015.
  19. Y. G. Sun, H. Y. Qiang., X. Mei, et al. “Modified repetitive learning control with unidirectional control input for uncertain nonlinear systems,” Neural Computation Application., V.30 ,No.6 pp.2003-2012, September, 2018.
  20. Y G Sun, W L Li, J Q XU, etc. “Nonlinear Dynamic Modeling and Fuzzy Sliding-Mode Controlling of Electromagnetic Levitation System of Low-Speed Maglev Train”. Journal of Vibro engineering, V. 19, No.1, PP. 328-342, February, 2017.
  21. A.Celikyilmaz, and LB. Turksen, "Modeling uncertainty with fuzzy logic," Studies in Fuzziness and Soft Computing, Springer Book.
  22. F. J. Lin, L. T. Teng and P. H. Sheh, "Intelligent Adaptive Back stepping Control System for Magnetic Levitation Apparatus," IEEE Transactionson Magnetics, V. 43, PP. 2009-20018, 2007.
  23. B. Hu, K. I. Mann, and R.G. Gosine "New Methodology for Analytical and Optimal Design of Fuzzy PID Controllers" IEEE Transaction on Fuzzy System, V. 7, No. 5, PP.521-538, 1999.
  24. T. Dereli , A Baykasoglu , K A1tun , A Durmusoglu, and I.B. Turksen, "Industrial applications of type-2 fuzzy sets and systems: A concise review," Computers in Induslly, vol. 62,pp.125-137, 2011.
  25. H. Hagras, "Type-2 FLCs: a new generation of fuzzy controllers," IEEE Comput. Intell. Mag.,V..2, issue.l, PP. 30-43,2007.
  26. S. Maity and J. Sil, "Color image segmentation using type-2 fuzzy sets, International Journal of Computer and Electrical.
  27. A.Kumar and V.Kumar, "Design and implementation of IT2FLC for Magnetic levitation system, Published in Advances in Electrical Engineering Systems Journal, World Science Publisher, United States, V. 1, No.2,PP. 116-123,2012.
  28. E. Cuevas, M. Cienfuegos, D. Zaldivar and M. Perez-Cisneros, "A swarm optimization algorithm inspired in the behavior of the social-spider", Expert Systems with Applications 40(16):6374–6384 (2013).

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