International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Tuning Fuzzy Control Rules via Genetic Algorithms: An Experimental Evaluation

Author Affiliations

  • 1Departamento de Ingeniería Industrial-Programa ingenieríamecatrónica, Universidad de Sonora, Unidad Regional Centro, Hermosillo, Sonora,MÉXICO
  • 2 Facultad De Ingeniería Universidad Autónoma De Campeche, Campeche, Campeche, MÉXICO
  • 3 Facultad de Ingeniería Campus Mexicali, Universidad Autónoma de baja California, Mexicali, Baja California, MÉXICO

Res. J. Recent Sci., Volume 2, Issue (10), Pages 81-87, October,2 (2013)


In this article, a simple genetic algorithm is used to solve an optimization problem that minimizes an objective function, with the purpose of obtaining the rule base of a fuzzy controller. The plant under consideration is an experimental direct current servosystem. The variable to be controlled is the load angular position. The objective function used here is the criterion IAE (Integrated Absolute Error). The genetic algorithm operates with populations who respond to consequents of the rule base.


  1. Lee C.C., Fuzzy Logic in Control Systems: Fuzzy Logic in Controller-Part 1, Transactions on Systems, Man, and Cybernetics, 20(1), 404-418 (1990)
  2. Lee C.C., Fuzzy Logic in Control Systems: Fuzzy Logic in Controller-Part 2, Transactions on Systems, Man, and Cybernetics, 20(2), 419-435 (1990)
  3. ShahaboddinShamshirband and Ali Za'fari, Evaluation of the Performance of Intelligent Spray Networks Based On Fuzzy Logic, Res.J.Recent Sci., 1(8), 77-81(2012)
  4. Sharifi M. and Shahriari B., Pareto Optimization of Vehicle Suspension Vibration for a Nonlinear Half-car Model Using a Multi-objective Genetic Algorithm, Res. J. Recent Sci.,1(8), 17-22 (2012)
  5. Zhan J. Y. and Li Y., Application of Genetic Algorithm in Optimization of Fuzzy Control Rules, Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, (ISDA06)(2006)
  6. Karr C.L, Design of a Adaptive fuzzy logic controller using a genetic algorithm, Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo CaliforniaUSA (1991)
  7. Lagunas-Jiménez R. and Pitalúa-Díaz N., Tuning Fuzzy Control Rules via Genetic Algorithms, Electronics, Robotics, and Automotive Mechanics Conference, Cuernavaca, Morelos, México, september (CERMA)(2007)
  8. Kuri Morales A., A Comprehensive Approach to Genetic Algorithms in Optimization and Learning Theory and applications, Instituto Politecnico Nacional, México,1(1999)
  9. Lagunas R., Fernández_Anaya G. and MartínezGarcía J.C., Experimental Evaluation of a Mixed H2/H-Based PID Using Genetic Algorithms, Proceedings of the IASTED International Conference, Circuits, Signals and Systems, May 19-21, Cancun México (2003)
  10. Chen C., Linear System Theory and Design, Oxford University Press, Third Edition (1999)
  11. Fuzzy Logic Toolbox user’s guide, Mathworks Inc., USA(2007)
  12. Bhattacharya Sourabh, Applications of DSTATCOM Using MATLAB/Simulation in Power System, Res. J. Recent Sci., 1(ISC-2011), 430-433 (2012)
  13. Mamdani, E. H., Application of Fuzzy Algorithm for Control of Simple Dynamic Plant, Proc. IEE.,121(12), 1585-1588(1974)
  14. Mamdani, E. H.and assilian, A Fuzzy Logic Controller for a Dynamic Plant, Intl. J. Man Machine stud., 1-13(1975)
  15. Passino M. K., Yurkovich S., Fuzzy Control, Addison-Wesley, USA(1998)
  16. PatilPallavi and Ingle Vikal, Obtaining a high Accurate Fault Classification of Power Transformer based on Dissolved Gas Analysis using ANFIS, Res.J.Recent Sci., 1(2), 97- 99(2012)
  17. Pedrycz W., Fuzzy control and Fuzzy systems, John Wiley & Sons Inc. second edition, USA(1996)
  18. CPU12 Reference Manual, MOTOROLA Inc. (1997)