Modelling of an Optimum Fuzzy Logic Controller Using Genetic Algorithm

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 749)


Fuzzy logic control is an increasingly popular technique in the past decades since it has a linguistic based structure and its performance is quite robust for nonlinear systems. For many real-world control problems, it is possible to find a working Fuzzy Logic Controller (FLC) by formulating heuristic knowledge and by using a “trial and error” approach for fine-tuning. This may not, however, always yield the anticipated results and is undoubtedly a tedious task because of the huge number of tuning parameters involved. To overcome this problem, a number of advanced approaches have been reported in the literature. This present work deals with optimization of a fuzzy logic controller with the help of genetic algorithm to control the liquid level of a tank. The fuzzy logic model developed by Takagi-Sugeno (T-S) has been used here. The parameters of T-S type fuzzy logic controller have been optimized within a defined range using genetic algorithm, and the results are discussed here.


Fuzzy Logic Controller (FLC) Genetic Algorithm (GA) Optimised FLC Type FLC Real-world Control Problems 
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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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Engineering and ScienceVictoria UniversityMelbourneAustralia

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