Practical Fuzzy Controller Development

  • Ian S. Shaw
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 457)


Among the fuzzy systems discussed earlier, rule-based fuzzy controllers are the most effective and practical forms of fuzzy control applicable to industrial systems. Such controllers may be implemented either in software or hardware. The question should arise as to what the trade-offs are between fuzzy implementations in software and hardware. The performance of a fuzzy controller depends, to a very great degree, on its tuning. In designing a fuzzy controller many more choices and options exist than in the case of conventional controllers. The design and optimization (i.e. tuning) of a fuzzy system is burdened by the many degrees of freedom:
$$F = kx{k_1}xrx{r_1}x{r_2}xmxpxd $$
where m = number of input variables; p = number of output variables; k = number of membership functions for each variables; k 1 = shape of membership functions for each variable; r = number of fuzzy rules; r 1 = choices of inference expressed in the fuzzy rule structure; r 2 = degree of support associated with each rule; d = choice of defuzzication method. Many of these choices are based on existing empirical data and design guidelines.


Membership Function Fuzzy Logic Fuzzy Rule Fuzzy Control Fuzzy Controller 
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.


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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Ian S. Shaw
    • 1
  1. 1.Industrial Electronic Technology Research GroupRand Afrikaans UniversityJohannesburgRepublic of South Africa

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