Browse > Article
http://dx.doi.org/10.5370/KIEE.2015.64.1.090

Optimal Design of Magnetic Levitation Controller Using Advanced Teaching-Learning Based Optimization  

Cho, Jae-Hoon (smart Logistics Technology Institute, Hankyong National University)
Kim, Yong-Tae (Department of Electrical, Electronic and Control Engineering, Hankyong National University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.64, no.1, 2015 , pp. 90-98 More about this Journal
Abstract
In this paper, an advanced teaching-learning based optimization(TLBO) method for the magnetic levitation controller of Maglev transportation system is proposed to optimize the control performances. An attraction-type levitation system is intrinsically unstable and requires a delicate control. It is difficult to completely satisfy the desired performance through the methods using conventional methods and intelligent optimizations. In the paper, we use TLBO and clonal selection algorithm to choose the optimal control parameters for the magnetic levitation controller. To verify the proposed algorithm, we compare control performances of the proposed method with the genetic algorithm and the particle swarm optimization. The simulation results show that the proposed method is more effective than conventional methods.
Keywords
Teaching-learning based optimization(TLBO); Clonal selection; Magnetic levitation controller; Maglev system; Intelligent optimization methods;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 R. V. Rao, V. J. Savsani, and D. P. Vakharia., “Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems”, Information Sciences, vol. 183, no. 1, pp. 1-15, 2012.   DOI   ScienceOn
2 Rong-Jong Wai, Jeng-Dao Lee, and Kun-Lun Chuang., “Real-time PID control strategy for maglev transportation system via particle swarm optimization”, IEEE Transactions on Industrial Electronics, vol. 58, no. 2, pp. 629-646, 2011.   DOI   ScienceOn
3 K. Chandrasekar and N. V. Ramana., “Performance Comparison of GA, DE, PSO and SA Approaches in Enhancement of Total Transfer Capability using FACTS Devices”, Journal of Electrical Engineering & Technology, vol. 7, no. 4, pp. 493-500, 2012.   DOI   ScienceOn
4 K. Lakshmi and S. Vasantharathna., “Hybrid Artificial Immune System Approach for Profit Based Unit Commitment Problem”, Journal of Electrical Engineering & Technology, vol. 8, no. 5, pp. 959-968, 2013.   DOI   ScienceOn
5 R. V. Rao, V. J. Savsani, and D. P. Vakharia., “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems”, Computer-Aided Design, vol. 43, pp. 303-315, 2011.   DOI   ScienceOn
6 E. Alvarez-Sanchez, J. Alvarez-Gallegos, R, Castro-Linares, “Modeling and controller design of a magnetic levitation system”, IEEE International Conference on Electrical and Electronics Engineering, pp. 330-334, 2005.
7 R. V. Rao and Vivek Patel., “Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm”, Applied Mathematical Modelling, vol. 37, no. 3, pp. 1147-1162, 2013.   DOI   ScienceOn
8 L. N. de Castro and Fernando J. Von Zuben, “Learning and Optimization Using the Clonal Selection Principle”, IEEE Trans. Evolutionary Computation, vol. 6, no. 3, pp. 239-251, 2002.   DOI   ScienceOn
9 Jae-Hoon Cho, Yong-Tae Kim, “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.   DOI   ScienceOn
10 Jae-Hoon Cho, Yong-Tae Kim, “Design of Levitation Controller with Optimal Fuzzy PID Controller for Magnetic Levitation System”, Journal of Korean Institute of Intelligent Systems, vol. 24, no. 3, pp. 279-284, 2014.   DOI   ScienceOn
11 El Hajjaji, Ahmed, and M. Ouladsine., “Modeling and nonlinear control of magnetic levitation systems”, IEEE Transactions on Industrial Electronics, vol.48, no. 4, pp. 831-838, 2001.   DOI   ScienceOn
12 Hyung-Woo Lee, Ki-Chan Kim, and Ju Lee., “Review of maglev train technologies", IEEE Transactions on Magnetics, vol. 42, no. 7, pp. 1917-1925, 2006.   DOI   ScienceOn
13 S. Kusagawa, J. Baba, E. Masada, “Weighty Reduction of EMS-Type Maglev Vehicle with a Novel Hybrid Control Scheme for Magnets”, IEEE Transactions on Magnetics, vol. 40, no. 4, pp. 3066-3068, 2004.   DOI   ScienceOn