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The Performance Improvement of Fuzzy Controller using the Shifting Method of Rule Base Table  

Che Wen-Zhe (Department of Electronics Engineering, Inha University)
Lee Chol-U (Department of Electronics Engineering, Inha University)
Kim Heung-Soo (Department of Electronics Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.42, no.6, 2005 , pp. 55-62 More about this Journal
Abstract
It is essential for a fuzzy logic controller to have an appropriate set of rules to perform at the desired level. The linguistic structure of the fuzzy logic controller allows a tentative linguistic policy to be used as an initial rule base. At the design stage, if one can reasonably assemble a good collection of rules, it may then be possible to be tuned to improve the controller performance. In this paper, we proposed the shifting method of rule base table to improve the performance of fuzzy controller. The proposed method is based on the principle of that the effect of the output to regulate the system would be greater when the error increases and the effect of output would be less when the error decreases. According to simulation results, it is an effective method to improve the fuzzy control rule base and the performance of fuzzy logic controllers.
Keywords
Fuzzy controller; Control rule table; Rules shifting;
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1 K. Shimojima, T. Fukuda, and Y.Hasegama, 'Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm,' Fuzzy Sets and Systems, Vol.71, PP. 295-300, May, 1995   DOI   ScienceOn
2 Jianwei Zhang, Alois Knoll, 'Constructing fuzzy controllers with B-spline models-principles and applications,' International Journal of Intelligent Systems, Vo1.13, pp. 257-285, March 1998   DOI   ScienceOn
3 L. A. Zadeh, 'Outline of a new approach to the analysis of complex systems and decision proceses,' IEEE Trans. syst., Man, Cybern, Vol. 3, pp. 28-44, Jan. 1973
4 Ashrafzadeh, F, Nosicki, E.P, 'Reducing the Order of Fuzzy Logic Controllers Based on Control Surface Rotation,' Electrical and Computer Engineering, IEEE 1997 Canadian Conference on Vol. 2, pp. 785-787, May, 1997   DOI
5 Jen-Yang Chen, 'Rule regulation of fuzzy sliding mode controller design: direct adaptive approach,' Fuzzy Sets and Systems, Vol.120, pp. 159-168, May 2001   DOI   ScienceOn
6 A. Lekova and D. Batanov, 'Self-testing and self-learning fuzzy expert system for technological process control,' Computers in Industry, Vol. 37, pp.135-141, Sep, 1998   DOI   ScienceOn
7 T. C. Chin and X. M. Qi, 'Genetic algorithms for learning the rule base of fuzzy logic controller,' Fuzzy Sets and Systems, Vol. 97, pp. 1-7, July 1998   DOI   ScienceOn
8 'Fuzzy sets as a basis for a theory of possibility,' Fuzzy Sets System., Vol.1, pp.3-28, 1978   DOI   ScienceOn
9 Shie-Jue Lee and Chen-Sen Ouyang, 'A neuro-fuzzy system modeling with self-constructing rule generation and hybrid SVD-based learning,' IEEE Trans. Fuzzy systems, Vol. 11, pp. 341-353, June 2003   DOI   ScienceOn