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자기부상시스템을 위한 교수-학습 최적화 알고리즘 기반의 퍼지 PID 제어기 설계
Design of TLBO-based Optimal Fuzzy PID Controller for Magnetic Levitation System 원문보기

전기학회논문지 = The Transactions of the Korean Institute of Electrical Engineers, v.66 no.4, 2017년, pp.701 - 708  

조재훈 (Smart Logistics Technology Institute, Hankyong National University) ,  김용태 (Department of Electrical, Electronic and Control Engineering, Hankyong National University)

Abstract ▼ AI-Helper 아이콘AI-Helper

This paper proposes an optimum design method using Teaching-Learning-based optimization for the fuzzy PID controller of Magnetic levitation rail-guided vehicle. Since an attraction-type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through...

주제어

#Teaching-learning-based optimization   #Fuzzy PID controller   #Magnetic levitation   #Rail-guided vehicle  

AI 본문요약
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제안 방법

  • Many studies dealing with Maglev system are based on linearization model using a Taylor series. In this paper, we use the linearized Maglev model based on a Taylor series of the actual nonlinear dynamic model and force distribution at nominal operating points. Fig.

이론/모형

  • In the paper, a fuzzy PID controller is proposed for the Maglev system and optimum gains of fuzzy PID controller are selected by TLBO algorithm. The fuzzy PID controller with the four scaling parameters carries out the control of Maglev system and the optimal parameters of the fuzzy PID controller are selected by TLBO.
  • In the paper, a fuzzy PID controller with fixed parameters is applied and then the optimum gains of fuzzy PID controller are selected by Teaching-Learning-based Optimization (TLBO) method. For the fitness function of TLBO, the performance index of PID controller is used.
  • To verify the proposed control method, a Maglev model was established by using MATLAB/Simulink and global optimization toolbox for simulation of gentic algorithm(GA) and particle swarm optimization(PSO). Table 2 shows initial parameters for the proposed algorithm and GA.
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참고문헌 (15)

  1. Lee, Hyung-Woo, Ki-Chan Kim, and Ju Lee, "Review of maglev train technologies," IEEE transactions on magnetics, vol. 42, No. 7, pp. 1917-1925, 2006. 

    상세보기 crossref

  2. Cho HW, Han HS, Lee JM, Kim BS, Sung SY, "Design considerations of EM-PM hybrid levitation and propulsion device for magnetically levitated vehicle," IEEE Transactions on Magnetics, vol. 45 No. 10, pp. 4632-4635, 2009. 

    상세보기 crossref

  3. Cho HW, Kim CH, Han HS, Lee JM, Kim BS, Kim DS, Lee YS, "Design and Characteristic Analysis of Hybrid-Type Levitation and Propulsion Device for High-Speed Maglev Vehicle," The Transactions of Korean Institute of Electrical Engineers, vol. 59, No. 4, pp. 715-721, 2010. 

  4. Tozoni OV, "Amlev-a self-regulating version of maglev," IEEE transactions on magnetics, vol. 37 No. 6, pp. 3925-3933, 2001. 

    상세보기 crossref

  5. Danfeng Z, Jie L, "Analysis of the low-frequency vibration of ems maglev vehicles," Control and Automation, 2007. ICCA 2007, pp 3157-3161, 2007. 

  6. Paddison JE, Ohsaki H, Masada E, "Control strategies for maglev electromagnetic suspension bogies," Decision and Control, 1996., Proceedings of the 35th IEEE Conference on, IEEE, vol. 3, pp. 2796-2797, 1996. 

  7. Taghirad H, Abrishamchian M, Ghabcheloo R, Toosi K, "Electromagnetic levitation system: An experimental approach," Proceedings of the 7th international Conference on Electrical Engineering, Power System Vol, pp. 19-26, 1998. 

  8. Chen H, Hao A, Long Z, "The controller design and performance index analysis of maglev train's suspension system," Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on, IEEE, vol. 1, pp 596-599, 2004. 

  9. Hurley WG, Hynes M, Wole WH, "Pwm control of a magnetic suspension system," IEEE Transactions on education, vol. 47, No. 2, pp. 165-173, 2004. 

    상세보기 crossref

  10. Zhang X, Liu H, Li Y, "Adaptive current-loop design for an electromagnetic suspension system," Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on, IEEE, vol. 1, pp. 341-344, 2011. 

  11. Lee BK, Kim IH, Kim JH, "Stability analysis and proposal of the simplified form of a fuzzy pid controller with fixed parameters," Journal of Korean Institute of Intelligent Systems, vol. 14, No. 7, pp. 807-815, 2004. 

    원문보기 상세보기 crossref

  12. Xu JX, Hang CC, Liu C, "Parallel structure and tuning of a fuzzy pid controller," Automatica, vol. 36, No. 5, pp. 673-684, 2000. 

    상세보기 crossref

  13. Kim JH, "A suggestion of nonlinear fuzzy pid controller to improve transient responses of nonlinear or uncertain systems," Journal of Korean Institute of Intelligent Systems, vol. 5, No. 4, pp. 87-100, 1995. 

  14. Cho JH, Kim YT, "Design of pid controller for magnetic levitation RGV using genetic algorithm based on clonal selection," Journal of Korean Institute of Intelligent Systems, vol. 22, No. 2, pp. 239-245, 2012. 

    원문보기 상세보기 crossref

  15. Rao RV, Savsani VJ, Vakharia D, "Teaching learningbased optimization: a novel method for constrained mechanical design optimization problems," Computer-Aided Design, vol. 43, No. 3 pp. 303-315, 2011. 

    상세보기 crossref

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