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Modelling of an Optimum Fuzzy Logic Controller Using Genetic Algorithm

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

Abstract

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.

Keywords

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Engineering and ScienceVictoria UniversityMelbourneAustralia

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