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Hybrid Coarse and Fine Controller Tuning Strategy for Magnetic Levitation System

  • Shradha KishoreEmail author
  • Vijaya Laxmi
Research Paper
  • 6 Downloads

Abstract

The magnetic levitation system requires a finely tuned controller to suspend a ball in the air. Any imbalance in the force balance condition might result in high fluctuations causing the ball to fall out of levitation. A hybrid tuning strategy based on successful levitation and minimization of vibrations has been designed and tested in real time. The proportional–integral–derivative controller has been tuned in two stages. The parameters of the controller have been calculated by the traditional pole placement technique for coarse tuning and evaluation of bounds. Nature-based non-traditional optimization techniques have been used to minimize the integral of the absolute error within this threshold limit for finer tuning. This novel strategy has been employed to find the best combination of the controller parameters such that levitation is ensured by coarse tuning and error is minimized by fine tuning. Real-time robustness analysis has also been done by means of rejection of external disturbance induced manually in the levitated stage.

Keywords

Magnetic levitation system Hybrid tuning Non-traditional optimization techniques Integral of the absolute error Coarse tuning 

Notes

Funding

This research work has not been funded by any agency.

Compliance with Ethical Standards

Conflict of interest

There are no conflicts of interest.

References

  1. Astrom K, Hagglund T (1995) PID controllers: theory, design and tuning, 2nd edn. Instrument Society of America, PittsburghGoogle Scholar
  2. Atherton DP, Majhi S (1999) Limitations of PID controllers. In: Proceedings of the American control conference. San Diego, California, pp 3843–3847Google Scholar
  3. Bennett S (2000) The past of PID controllers. Ann Rev Control 25:43–53CrossRefGoogle Scholar
  4. Duarte-Mermoud MA, Prieto RA (2004) Performance index for quality response of dynamical systems. ISA Trans 43:133–151CrossRefGoogle Scholar
  5. Gao J, Tao T, Mei X, Jiang GM, Xu ZL (2012) A new method using pole placement technique to tune multi-axis PID parameter for matched servo dynamics. J Mech Eng Sci 227:1681–1696CrossRefGoogle Scholar
  6. Ghosh A, Krishnan TR (2014) Design and implementation of a 2-DOF PID compensation for magnetic levitation systems. ISA Trans 53:1216–1222CrossRefGoogle Scholar
  7. Hajjaji A, Ouladsine M (2001) Modeling and nonlinear control of magnetic levitation systems. IEEE Trans Ind Electron 48:831–838CrossRefGoogle Scholar
  8. Hassanzadeh I, Mobayen S (2008) Design and implementation of a controller for magnetic levitation system using genetic algorithms. J Appl Sci 8:4644–4649CrossRefGoogle Scholar
  9. Hwang S, Fang S (1994) Closed-loop tuning method based on dominant pole placement. Chem Eng Commun 136:45–66CrossRefGoogle Scholar
  10. Kristinsson K, Dumont G (1992) System identification and control using genetic algorithms. IEEE Trans Syst Man Cybern 22:1033–1046CrossRefGoogle Scholar
  11. Li Y, Ang K, Chong G (2005) PID control system analysis, design and technology. IEEE Trans on Control Syst Technol 13:559–576CrossRefGoogle Scholar
  12. Lopez A, Miller J, Smith C, Murill P (1967) Tuning controllers with error-integral criteria. Instrum Technol 14:57–62Google Scholar
  13. Lu Y, Dang Q (2019) Design of a novel single-angle inclination HTS Maglev train PMG Turnout. Iran J Sci Technol Trans Electr Eng 43:507–516CrossRefGoogle Scholar
  14. Magnetic Levitation: Control Experiments (2011) UK: Feedback Instruments Limited, UK.Google Scholar
  15. Maji L, Roy P (2015) Design of PID and FOPID controllers based on bacterial foraging and particle swarm optimization for magnetic levitation system. In: Indian control conference. IIT Madras, pp 463–468Google Scholar
  16. Mirjalili S, Mirjalili SM (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61CrossRefGoogle Scholar
  17. Naumovic MB, Veselic BR (2008) Magnetic levitation system in control engineering education. Autom Control Robot 7:151–160Google Scholar
  18. Nicolau V (2013) On PID controller design by combining pole placement technique with symmetrical optimum criterion. Math Prob Eng Article ID 316827:1–9zbMATHGoogle Scholar
  19. Ogata K (1997) Modern control engineering. PHI Learning Private Ltd, New JerseyzbMATHGoogle Scholar
  20. Oliveira PB, Freire H, Pires EJS (2016) Grey wolf optimization for PID controller design with prescribed robustness margins. Soft Comput 20:4243–4255CrossRefGoogle Scholar
  21. Pati A, Negi R (2019) An optimised 2-DOF IMC-PID-based control scheme for real-time magnetic levitation system. International Journal of Automation and Control 13:413–439CrossRefGoogle Scholar
  22. Rao SS (2009) Engineering optimization: theory and practice. Wiley, New JerseyCrossRefGoogle Scholar
  23. Roy P, Borah M, Majhi L, Singh N (2015) Design and implementation of FOPID controllers by PSO, GSA and PSOGSA for MagLev system. In: International symposium on advanced computing and communication (ISACC), Silchar, pp 10–15Google Scholar
  24. Sain D, Swain SK (2016) TID and I-TD controller design for magnetic levitation system using genetic algorithm. Perspect Sci 8:370–373CrossRefGoogle Scholar
  25. Starbino AV, Sathiyavathi S (2019) Real-time implementation of SMC–PID for magnetic levitation system. Sadhana 44:115.  https://doi.org/10.1007/s12046-019-1074-4
  26. Sujitjorn S, Wiboonjaroen W (2011) State PID feedback for pole placement of LTI systems. Math Probl Eng Article ID 929430:763–772Google Scholar
  27. Swain S, Sain D (2017) Real time implementation of fractional order PID controllers for a magnetic levitation plant. Int J Electron Commun 78:141–156CrossRefGoogle Scholar
  28. Tan W, Liu J, Chen T, Marquez HJ (2006) Comparison of some well-known PID tuning formulas. Comput Chem Eng 30:1416–1423CrossRefGoogle Scholar
  29. Tsai JF, Carlsson J, Ge D, Hu Y-C, Shi J (2012) Optimization Theory: Methods, and Applications in Engineering. Math Probl Eng Article ID 345858(2012):1–8Google Scholar
  30. Valasek M, Olgac N (1995) Efficient pole placement techique for linear time- variant SISO systems. IEEE Proc Control Theory Appl 142:451–458CrossRefGoogle Scholar
  31. Wang QG, Zhang Z, Astrom KJ (2008) Guarenteed dominant pole placement with PID controllers. The International Federation of Automatic Control, Seoul, KoreaGoogle Scholar
  32. Wu H, Su W, Liu Z (2014) PID controllers: design and tuning methods. In: IEEE conference on industrial electronics and Applications. Hangzhou, ChinaGoogle Scholar
  33. Wong TH (1986) Design of a magnetic levitation control system- an undergraduate project. IEEE Trans Educ E29:196–200CrossRefGoogle Scholar
  34. Yadav S, Verma S (2016) Optimized PID controller for magnetic levitation system. IFAC-Pap Line 49:778–782CrossRefGoogle Scholar
  35. Ziegler J, Nichols N (1942) Optimum settings for automatic controllers. Am Soc Mech Eng 64:759–768Google Scholar

Copyright information

© Shiraz University 2019

Authors and Affiliations

  1. 1.Birla Institute of TechnologyMesra, Ranchi. Patna CampusIndia

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