Abstract
The article proposes a method for automatic synthesis of a fuzzy controller and optimization of its parameters based on an adaptive genetic algorithm with combined operators of random changes. A distinctive feature of the proposed method is the method of forming a knowledge base of a fuzzy controller based on statistical information about the operation of a real industrial facility.
The use of an adaptive genetic algorithm makes it possible to quickly and efficiently optimize the parameters of a fuzzy controller in such a way as to provide the best indicators of its practical use: minimum duration, absence of oscillation and steady-state error of the transient process.
The proposed method is automated due to the development of a special software application in the MATLAB modeling environment, and requires minimal human participation in its work. Simulation modeling is performed and the results confirming the correctness of the proposed method and the possibility of its practical use are presented. The work of the method can be simplified as a sequence of the following stages: the formation of the initial parameters of the fuzzy controller; the search for the optimal lengths of the term-sets of input-output linguistic variables; search for optimal parameters of term-sets of input-output linguistic variables. #CSOC1120.
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Acknowledgements
Scientific research was carried out as part of the project “Creating a high-tech production of hardware and software systems for processing agricultural raw materials based on microwave radiation” (Agreement with the Ministry of Education and Science of the Russian Federation № 075-11-2019-083 dated 20.12.2019, Agreement South Federal University № 18 dated 20.09.2019, number of work in South Federal University № HD/19-25-RT).
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Ignatyev, V.V., Beloglazov, D.A., Soloviev, V.V., Kovalev, A.V. (2021). Development of a Method for Automatic Generation and Optimization of Fuzzy Controller Parameters Based on an Adaptive Genetic Algorithm. In: Silhavy, R. (eds) Artificial Intelligence in Intelligent Systems. CSOC 2021. Lecture Notes in Networks and Systems, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-030-77445-5_38
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