Intelligent Tuning of Fuzzy Controllers by Learning and Optimization

  • Rodolfo HaberEmail author
  • Raúl Mario del Toro
  • Jorge Godoy
  • Agustín Gajate
Part of the Atlantis Computational Intelligence Systems book series (ATLANTISCIS, volume 9)


Fuzzy Logic Control (FLC) emerged as one of the most outstanding control techniques in the middle of 80s. The great amount of literature on FLC that has appeared is the main evidence of the increasing importance that fuzzy controllers have been given in the control system design field.


Membership Function Performance Index Fuzzy Control Fuzzy Controller Computer Numerical Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Ministry of Economy and Competitiveness through its DPI2012-35504 CONMICRO research project and the European project 295372 DEMANES. The authors wish to thank the reviewers and the book editors for their useful suggestions. We also gratefully acknowledge the collaboration of Antony Price in the preparation of this paper. J. Godoy wants to especially thank the JAE program (Spanish National Research Council—CSIC) for its support in the development of this work.


  1. Bezdek, J. C., Ehrlich, R., & Full, W. (1984). Fcm: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10, 191–203.Google Scholar
  2. Bonissone, P. P. (2000). Hybrid soft computing systems: Where are we going? Proceedings of the 14th European Conference on Artificial Intelligence (ECAI 2000) (Berlin, Germany) (pp. 739–746).Google Scholar
  3. Bonissone, P. P., Badami, V., Chiang, K. H., Khedkar, P. S., Marcelle, K. W., & Schutten, M. J. (1995). Industrial applications of fuzzy logic at general electric. Proceedings of the IEEE, 83, 450–465.CrossRefGoogle Scholar
  4. Bonissone, P. P., Chen, Y. U. T. O., Goebel, K., & Khedkar, P. S. (1999). Hybrid soft computing systems: Industrial and commercial applications. Proceedings of the IEEE, 87, 1641–1667.CrossRefGoogle Scholar
  5. Bonissone, P. P., Khedkar, P. S., & Chen, Y. (1996). Genetic algorithms for automated tuning of fuzzy controllers: A transportation application. Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, 1, 674–680.CrossRefGoogle Scholar
  6. Gajate, A., Haber, R., Toro, R. D., Vega, P., & Bustillo, A. (2012). Tool wear monitoring using neuro-fuzzy techniques: A comparative study in a turning process. Journal of Intelligent Manufacturing, 23, 869–882.CrossRefGoogle Scholar
  7. Gajate, A., Haber, R. E., Vega, P. I., & Alique, J. R. (2010). A transductive neuro-fuzzy controller: Application to a drilling process. IEEE Transactions on Neural Networks, 21, 1158–1167.CrossRefGoogle Scholar
  8. Haber, R. E., del Toro, R. M., & Gajate, A. (2010). Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process. Information Sciences, 180, 2777–2792.CrossRefGoogle Scholar
  9. Jang, J.-S. R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. doi: 10.1109/21.256541.CrossRefGoogle Scholar
  10. Kasabov, N. K., & Song, Q. (2002). Denfis: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction. IEEE Transactions on Fuzzy Systems, 10(2), 144–154. doi: 10.1109/91.995117.CrossRefGoogle Scholar
  11. Keller, J. M., Gray, M. R., & Givens, J. A. (1985). Fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man and Cybernetics, 15, 580–585.CrossRefGoogle Scholar
  12. Kim, H. M., Dickerson, J., & Kosko, B. (1996). Fuzzy throttle and brake control for platoons of smart cars. Fuzzy Sets and Systems, 84(3), 209–234. doi: 10.1016/issn=0165-0114,0165-0114(95)00326-6.CrossRefGoogle Scholar
  13. King, P. J., & Mamdani, E. H. (1977). The application of fuzzy control systems to industrial processes. Automatica, 13, 235–242.CrossRefGoogle Scholar
  14. Kosko, B. (1991). Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Upper Saddle River, NJ, USA: Prentice-Hall Inc. ISBN 0-13-611435-0.Google Scholar
  15. Kroese, D. P., Rubinstein, R. Y., & Taimre, T. (2007). Application of the cross-entropy method to clustering and vector quantization. Journal of Global Optimization, 37, 137–157.CrossRefzbMATHGoogle Scholar
  16. Lin, C., Jeng, F. L., Lee, C. S., & Raghavan, R. (1997). Hierarchical fuzzy logic water-level control in advanced boiling water reactors. Nuclear Technology, 118, 254–262.Google Scholar
  17. Lin, C. J., Chen, C. H., & Lin, C. T. (2008). Efficient self-evolving evolutionary learning for neurofuzzy inference systems. IEEE Transactions on Fuzzy Systems, 16, 1476–1490.CrossRefGoogle Scholar
  18. MacVicar-Whelan, P. J. (1979). Fuzzy logic: An alternative approach. Proceedings of The International Symposium on Multiple-Valued Logic (pp. 152–158).Google Scholar
  19. Maeda, M., & Murakami, S. (1992). A self-tuning fuzzy controller. Fuzzy Sets and Systems, 51, 29–40.CrossRefGoogle Scholar
  20. Mamdani, E. H., & Assilian, S. (1975). An experimental in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.CrossRefzbMATHGoogle Scholar
  21. Martin, A. G., & Guerra, R. E. H. (2009). Internal model control based on a neurofuzzy system for network applications. A case study on the high-performance drilling process. IEEE Transactions on Automation Science and Engineering, 6, 367–372.CrossRefGoogle Scholar
  22. Miyamoto, S., Yasunobu, S., & Ihara, H. (1987). Predictive fuzzy control and its application to automatic train operation systems. Boca Raton: FL, USA (CRC Press Inc.).Google Scholar
  23. Morari, M., & Zafiriou, E. (1989). Robust process control. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  24. Narendra, K. S., Balakrishnan, J., & Ciliz, M. K. (1995). Adaptation and learning using multiple models, switching, and tuning. IEEE Control Systems Magazine, 15, 37–51.CrossRefGoogle Scholar
  25. Roychowdhury, S., & Pedrycz, W. (2001). A survey of defuzzification strategies. International Journal of Intelligent Systems, 16, 679–695.CrossRefzbMATHGoogle Scholar
  26. Rubinstein, R. (2005). A stochastic minimum cross-entropy method for combinatorial optimization and rare-event estimation. Methodology and Computing in Applied Probability, 7, 5–50.CrossRefzbMATHMathSciNetGoogle Scholar
  27. Shaw, I. S. (1998). Fuzzy control of industrial systems: theory and applications. Norwell: Kluwer Academic Publishers.Google Scholar
  28. Song, Q., & Kasabov, N. (2006). Twnfi - a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling. Neural Networks, 19, 1591–1596.CrossRefzbMATHGoogle Scholar
  29. Sugeno, M. (1985). An introductory survey of fuzzy control. Information Sciences, 36, 59–83.CrossRefzbMATHMathSciNetGoogle Scholar
  30. Sugeno, M., & Kang, G. T. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28(1), 15–33. doi: 10.1016/0165-0114(88)90113-3.CrossRefzbMATHMathSciNetGoogle Scholar
  31. Verdegay, J. L., Yager, R. R., & Bonissone, P. P. (2008). On heuristics as a fundamental constituent of soft computing. Fuzzy Sets and Systems, 159, 846–855.CrossRefMathSciNetGoogle Scholar
  32. Wang, P. P., & Tyan, C. Y. (1994). Fuzzy dynamic system and fuzzy linguistic controller classification. Automatica, 30, 1769–1774.CrossRefzbMATHGoogle Scholar
  33. Yager, R. R., Filev, D. P. (1994). Essentials of fuzzy modeling and control. New York: Wiley.Google Scholar
  34. Zadeh, L. A. (1988). Fuzzy logic. IEEE Computer, 21(4), 83–93.CrossRefGoogle Scholar
  35. Zadeh, L. A. (1994). Soft computing and fuzzy logic. IEEE Software, 11, 48–56.CrossRefGoogle Scholar
  36. Zhu, L., Chung, F. L., & Wang, S. (2009). Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 39, 578–591.CrossRefGoogle Scholar

Copyright information

© Atlantis Press and the authors 2014

Authors and Affiliations

  • Rodolfo Haber
    • 1
    Email author
  • Raúl Mario del Toro
    • 1
  • Jorge Godoy
    • 1
  • Agustín Gajate
    • 1
  1. 1.Center for Automation and RoboticsConsejo Superior de Investigaciones Científicas—Universidad Politécnica de Madrid (CSIC—UPM)MadridSpain

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