Browse > Article
http://dx.doi.org/10.5916/jkosme.2014.38.6.716

GA-based parameter identification of DC motors  

Lee, Yun-Hyung (Education & Research Team, Korea Institute of Maritime and fisheries Technology)
So, Myung-Ok (Division of Marine Engineering, Korea Maritime and Ocean University)
Publication Information
Journal of Advanced Marine Engineering and Technology / v.38, no.6, 2014 , pp. 716-722 More about this Journal
Abstract
In order to design the speed controller of the DC motor system, firstly, parameters estimation of the system must be preceded. In this paper, we proposed the application of genetic algorithm(GA) optimization in estimating the parameters of DC motor. Estimated models are considered both first and second order models, and each estimated model is optimized by minimizing three different types of the evaluation function of GA. Also, GA is imported in comparison with estimation result of numerical analysis method because of its power in searching entire solution space with more probability of finding the global optimum. Data for parameter estimation is acquired from input and output signals of the actual experiment device and the butterworth filter also designs for removing noise in the signals. Finally comparison between real data of the actual device and estimated models is presented to indicate effectiveness and resolution of proposed identification method.
Keywords
DC motor system; Genetic algorithm; Model adjustment technique; Fitness function; Butterworth filter;
Citations & Related Records
연도 인용수 순위
  • Reference
1 I. F. EI-Arabawy, H. A. Yousef, M. Z. Mostafa, and H. M. AbduIkader, "Parameters estimation of nonlinear models of DC motors using neural networks," Proceedings of 26th Annual Conference of the IEEE Industrial Electronics Society, vol. 3, pp. 1997-2000, 2000.
2 G. Jin and S. Joo, "A study on a real-coded genetic algorithm", Journal of Control Automation and Systems Engineering, vol. 6, no. 4, pp. 268-275, 2000.   과학기술학회마을
3 G. G. Jin, Genetic Algorithm and It's Application, Kyowoosa, 2000.
4 Y. Nozaka, "Trend of new control theory application in industrial process control (survey)", Proceedings of International Federation of Automatic Control 12th Triennial World Congress. vol. 4, pp. 215-218, 1993.
5 K. J. Astrom and T. Hagglund, "Automatic tuning of simple regulators with specifications on phase and amplitude margins", Automatica, vol. 20, pp. 645-651, 1984.   DOI   ScienceOn
6 T. Yamamoto, M. Kaneda, T. Oki, E. Watanabe, and K. Tanaka, "Intelligent tuning PID controllers", Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 2610-2615, 1995.
7 E. H. Bristol, "Pattern recognition: an alternative to parameter identification in adaptive control", Automatica, vol. 13, pp. 197-202, 1977.   DOI   ScienceOn
8 A. Dupuis, M. Ghribi, and A. Kaddouri, "Multiobjective genetic estimation of DC motor parameters and load torque," Proceedings of IEEE International Conference on Industrial Technology, vol. 3, pp. 1511-1514, 2004.
9 M. S. A. Mohamad, I. M. Yassin, A. Zabidi, M. N. Taib, and R. Adnan, "Comparison between PSO and OLS for NARX parameter estimation of a DC motor," IEEE Symposium on Industrial Electronics & Applications, pp. 27-32, 2013.
10 M. Lankarany and A. Rezazade, "Parameter estimation optimization based on genetic algorithm applied to DC motor", Proceedings of International Conference on Electrical Engineering, pp. 1-6, 2007.