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Using Motor Speed Profile and Genetic Algorithm to Optimize the Fuzzy Logic Controller for Controlling DC Servomotor

by Thanh Trung Nguyen, Takashi Komeda
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 14
Year of Publication: 2014
Authors: Thanh Trung Nguyen, Takashi Komeda
10.5120/16408-6076

Thanh Trung Nguyen, Takashi Komeda . Using Motor Speed Profile and Genetic Algorithm to Optimize the Fuzzy Logic Controller for Controlling DC Servomotor. International Journal of Computer Applications. 94, 14 ( May 2014), 1-8. DOI=10.5120/16408-6076

@article{ 10.5120/16408-6076,
author = { Thanh Trung Nguyen, Takashi Komeda },
title = { Using Motor Speed Profile and Genetic Algorithm to Optimize the Fuzzy Logic Controller for Controlling DC Servomotor },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 14 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number14/16408-6076/ },
doi = { 10.5120/16408-6076 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:17:36.886002+05:30
%A Thanh Trung Nguyen
%A Takashi Komeda
%T Using Motor Speed Profile and Genetic Algorithm to Optimize the Fuzzy Logic Controller for Controlling DC Servomotor
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 14
%P 1-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper describes a new proposed algorithm to automatically tune a Fuzzy Logic Controller by using motor Speed profile and Genetic Algorithm (FLCSGA algorithm) in controlling a DC Servo Motor. In the new method, the tuning process of the Fuzzy Logic Controller (FLC) is divided into two consecutive stages which are tuning rule base and tuning Membership Functions (MFs). The tuning rule base (Fuzzy rules) is based on the motor speed profiles, and the Genetic Algorithm (GA) is used to optimize MFs. In addition, a new encoding method was suggested for the GA that reduces remarkably optimization time for the system. This is a very important thing, especially with the real experiments for optimizing system such as motors control. The experiments on a Maxon motor RE 35 273752 showed that after using FLCSGA algorithm, an optimized FLC was generated. This FLC that had better performances compared to using the conventional proportional-integralderivative controller (PID controller) in term of settling time, rise time. Besides, the required generations and the amount of chromosomes in population of GA are reduced significantly compared to some previous studies. It means the convergence time is very fast.

References
  1. Copperhill Technologies Corp. "A Comprehensible Guide to Servo Motor Sizing," , May 2007.
  2. C. Chen, T. Hong, and V. S. Tseng. A comparison of different fitness functions for extracting membership functions used in fuzzy data mining. In IEEE Symposium on foundations of computational intelligence, pages 550–555, 2007.
  3. Y. Chiou and L. Lan. Genetic fuzzy logic controller: an iterative evolution algorithm with new encoding method. Fuzzy Sets and Systems, 152(3):617–635, 2005.
  4. C. Liu, B. Li, and X. Yang. Fuzzy logic controller design based on genetic algorithm for dc motor. In IEEE Intl. Conf. On Electronics, Communications and Control, pages 2662–2665, 2011.
  5. D. E. Goldberg. Genetic Algorithms in search, optimization, and machine learning. Addison-Wesley, 1989.
  6. D. E. Goldberg. The Design of competent Genetic Algorithms: Steps towards a computational theory of innovation. Kluwer Academic, 2002.
  7. D. Pelusi. Genetic-neuro-fuzzy controllers for second order control systems. In 5th European Symposium on Computer Modeling and Simulation, pages 12–17, 2011.
  8. D. Pelusi. Improving settling and rise times of controllers via intelligent algorithms. In 14th Inter. Conf. On Modelling and Simulation, pages 187–192, 2012.
  9. F. Herrera, M. Lozano, and J. L. Verdegay. Tuning fuzzy logic controllers by genetic algorithms. Inte. Jour. Of Approximate Reasoning, pages 299–315, 2013.
  10. J. Jamaludin, N. A. Rahim, and W. P. Hew. Development of a self-tuning fuzzy logic controller for intelligent control of elevator systems. Engineering Applications of Artificial Intelligence, 22(8):1167–1178, 2009.
  11. J. Zhang, N. Wang, and S. Wang. A developed method of tuning pid controllers with fuzzy rules for integrating process. In Proc. IEEE Of American Control Conference, pages 1109 – 1114, 2004.
  12. K. Belarbi, F. Titel, W. Bourebia, and K. Benmahammed. Design of mamdani fuzzy logic controllers with rule base minimisation using genetic algorithm. Engineering Applications of Artificial Intelligence, 17(7):875880, 2005.
  13. L. Baron. Fuzzy decision support system knowledge base generation using a genetic algorithm. Inter. Journal of approximate reasoning, 24(2-3):125–148, 2001.
  14. M. F. Rahmat and M. M. Ghazaly. Performance comparison between pid and fuzzy logic controller in position control system of dc servomotor. Technology, pages 1–17, 2006.
  15. N. Ozturk and E. Celik. Speed control of permanent magnet synchronous motors using fuzzy controller based on genetic algorithms. Int. Journal of Electrical Power and Energy Systems, 43(1):889–898, 2012.
  16. O. Gundogdu and K. Erenturk. Fuzzy control of a dc motor driven four-bar mechanism. Mechatronics Journal, 15:423– 438, 2003.
  17. O. Wahyunggoro and N. B. Saad. Development of fuzzy-logicbased self tuning pi controller for servomotor. In IEEE Intl. Conf. On Control, Automation, Robotics and Vision, pages 1545–1550, 2004.
  18. P. C. Shill, K. K. Pal, M. F. Amin, and K. Murase. Genetic algorithm based fully automated and adaptive fuzzy logic controller. In IEEE Intl. Conf. On Fuzzy Systems, pages 1572– 1579, 2011.
  19. R. Malhotra, N. Singh, and Y. Singh. Design of embedded hybrid fuzzy-ga control strategy for speed control of dc motor: A servo control case study. Int. Journal of Computer Applications, 6(5):37–46, 2010.
  20. R. Malhotra, T. Kaur, and G. S. Deol. Dc motor control using fuzzy logic controller. Int. Journal. of advanced engineering sciences and technologies, 8(2):291–296, 2011.
  21. S. Boualegue, J. Haggge, M. Ayadi, and M. Benrejeb. Pid-type fuzzy logic controller tuning based on particle swarm optimization. Engineering Applications of Artificial Intelligence, 25(3):483–493, 2013.
  22. S. Chopra and R. Mitra. Fuzzy controller: choosing an appropriate and smallest rule set. Inter. Journal of computational cognition, 3(4):73–79, 2005.
  23. S. G. Gilbert. Uniform crossover in genetic algorithms. In Proc. Of the 3rd Inter. Conf. Genetic Algorithms, pages 2–9, 1989.
  24. S. Khan and et al. Design and implementation of an optimal fuzzy logic controller using genetic algorithm. Journal of Computer Siences, 4(10):799–806, 2008.
  25. S. N. Sivanandam, S. Sumathi, and S. N. Deepa. Introduction to Fuzzy Logic using MATLAB. Springer, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Motor Speed

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