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AKILLI SÄ°STEMLER VE UYGULAMALARI DERGÄ°SÄ°
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Optimization of Fuzzy Logic Controller by Using Heuristic Algorithms

Sezgisel Algoritmalar Kullanılarak Bulanık Mantık Denetleyici Optimizasyonu

How to cite: Ãœnsal S, Alışkan Ä. Optimization of fuzzy logic controller by using heuristic algorithms. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2019; 2(1): 13-18. DOI: 10.54856/jiswa.201905052

Full Text: PDF, in Turkish.

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Title: Optimization of Fuzzy Logic Controller by Using Heuristic Algorithms

Abstract: There are many design parameters in the structure of fuzzy logic controllers. Conventional methods that don't have a systematic approach are often used in determining of these parameters. However, setting the controller parameters in this way leads to long experiments and this takes a lot of time. For this reason, design parameters of the fuzzy logic controller are usually determined by using heuristic algorithms. Because, heuristic algorithms can offer solutions that are very close to the optimal solution for the problems where exact solution cannot be obtained. In this study, output membership functions of a fuzzy logic controller are optimized using particle swarm optimization and genetic algorithm. Design and optimization stages are explained in detail and results are compared with each other.

Keywords: Fuzzy logic; genetic algorithm; particle swarm optimization; heuristic algorithms


Başlık: Sezgisel Algoritmalar Kullanılarak Bulanık Mantık Denetleyici Optimizasyonu

Özet: Bulanık mantık denetleyicilerin yapısında birçok tasarım parametresi bulunmaktadır. Bu parametrelerin belirlenmesinde çoğunlukla sistematik bir yaklaşıma sahip olmayan geleneksel yöntemler kullanılmaktadır. Ancak, denetleyici parametrelerinin bu şekilde ayarlanması uzun denemelere yol açmakta ve bu durum oldukça fazla zaman almaktadır. Bu nedenle, bulanık mantık denetleyici tasarım parametrelerinin belirlenmesinde genellikle sezgisel algoritmalardan yararlanılmaktadır. Çünkü; sezgisel algoritmalar kesin çözümün elde edilemediği problemlerde optimum çözümün çok yakınında çözümler sunabilen algoritmalardır. Ele alınan çalışmada, bir bulanık mantık denetleyicinin çıkış üyelik fonksiyonları parçacık sürüsü optimizasyonu ve genetik algoritma kullanılarak optimize edilmiştir. Tasarım ve optimizasyon aşamaları detaylı bir şekilde anlatılarak elde edilen sonuçlar birbiriyle karşılaştırılmıştır.

Anahtar kelimeler: Bulanık mantık; genetik algoritma; parçacık sürüsü optimizasyonu; sezgisel algoritmalar


Bibliography:
  • Olivas EL, Castillo O, Valdez F, Soria J. Ant colony optimization for membership function design for a water tank fuzzy logic controller. In 2013 IEEE Workshop on Hybrid Intelligent Models and Applications (HIMA), April 16-19, 2013, Singapore, pp. 27-34.
  • Kapetanovic N, Osmic N, Konjicija S. Optimization of membership functions of Sugeno-Takagi fuzzy logic controllers with two inputs and one output using genetic algorithms. In 2014 X International Symposium on Telecommunications (BIHTEL), October 27-29, 2014, Sarajevo, Bosnia and Herzegovina, pp. 1-7.
  • Ullah A, Li J, Hussain A, Shen Y. Genetic optimization of fuzzy membership functions for cloud resource provisioning. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), December 6-9, 2016, Athens, Greece, pp. 1-8.
  • Paul AK, Shill PC. Optimizing fuzzy membership function using dynamic multi swarm-PSO. In 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), May 13-14, 2016, Dhaka, Bangladesh, pp. 139-144.
  • Safaee B, Mashhadi SKM. Fuzzy membership functions optimization of fuzzy controllers for a quad rotor using particle swarm optimization and genetic algorithm. In 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA), January 27-28, 2016, Qazvin, Iran, pp. 256-261.
  • Caner M, Gulseren U. The optimization of fuzzy PSS rule table using genetic algorithm. Afyon Kocatepe Universitesi Fen ve Muhendislik Bilimleri Dergisi 2010; 10(1): 83-92.
  • Pitalua-Diaz N, Lagunas-Jimenez R. Tuning fuzzy control rules via genetic algorithms. In Electronics, Robotics and Automotive Mechanics Conference (CERMA 2007), September 25-28, 2013, Cuernavaca, Mexico, pp. 364-369.
  • Yeasmin S, Pau AK, Shill PC. Optimization of interval type-2 fuzzy logic controllers with rule base size reduction using genetic algorithms. In 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), September 22-24, 2016, Dhaka, Bangladesh, pp. 1-6.
  • Ghani NMA, Nasir ANK, Tokhi MO. Optimization of fuzzy logic scaling parameters with spiral dynamic algorithm in controlling a stair climbing wheelchair: Ascending task. In 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), September 2-5, 2014, Miedzyzdroje, Poland, pp. 776781.
  • Sebastiao A, Lucena C, Palma L, Cardoso A, Gil P. Optimal tuning of scaling factors and membership functions for Mamdani type PID fuzzy controllers. In 2015 International Conference on Control, Automation and Robotics, May 20-22, 2015, Singapore, pp. 92-96.
  • Salleh Z, Sulaiman M, Omar R, Patakor FA. Optimization of fuzzy logic based for vector control induction motor drives. In 2016 8th Computer Science and Electronic Engineering (CEEC), September 28-30, 2016, Colchester, UK, pp. 83-88.
  • Pelusi D. Optimization of a fuzzy logic controller using genetic algorithms. In 2011 Third International Conference on Intelligent Human-Machine Systems and Cybernetics, August 26-27, 2011, Hangzhou, China, 143-146.
  • Valdez F, Melin P, Castillo O. Particle swarm optimization for designing an optimal fuzzy logic controller of a DC motor. In 2012 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), August 6-8, 2012, Berkeley, CA, USA, pp. 1-6.
  • Chaiyatham T, Ngamroo I. A bee colony optimization based fuzzy logic-PID control design of electrolyzer for microgrid stabilization. International Journal of Innovative Computing, Information and Control 2012; 8(9): 6049-6066.
  • Rajan S, Sahadev S. Performance improvement of fuzzy logic controller using neural network. International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST-2015), December 9-11, 2015, Trissur, India, pp.704-714.
  • Nguyen DH. A comparison of DE and SFLA optimization algorithms in tuning parameters of fuzzy logic controller. International Journal of Computer Applications 2016; 156(11): 17-22.
  • Karaboga D. Yapay Zeka Optimizasyon Algoritmalari. Nobel Yayin Dagitim, 2011.
  • Akyol S, Alatas B. The current swarm intelligence optimization algorithms. Nevsehir University Fen Bilimleri Enstitusu Dergisi 2012; 1: 36-50.
  • Kennedy J, Eberhart RC. Particle swarm optimization. In Proceedings of ICNN'95 - International Conference on Neural Networks, November 27-December 1, 1995, Perth, WA, Australia, pp. 1942-1948.
  • Holland JH. Adaptation In Natural And Artificial Systems. University of Michigan Press, Ann Arbor, USA, 1975.
  • Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, New York, USA, 1989.
  • Aliskan I, Unsal S. Speed control of permanent magnet synchronous motor by using fuzzy logic controllers having different inference methods. Pamukkale University Journal of Engineering Sciences 2018; 24(2): 185-191.