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.
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Ganguly, P., Kalam, A., Zayegh, A. (2019). Modelling of an Optimum Fuzzy Logic Controller Using Genetic Algorithm. In: Chattopadhyay, S., Roy, T., Sengupta, S., Berger-Vachon, C. (eds) Modelling and Simulation in Science, Technology and Engineering Mathematics. MS-17 2017. Advances in Intelligent Systems and Computing, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-319-74808-5_28
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DOI: https://doi.org/10.1007/978-3-319-74808-5_28
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