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Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning
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DOI：10.3390/bdcc9010017.png)
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In this article, a fuzzy controller mathematical model synthesising method that uses cognitive computing and a genetic algorithm for automated tuning and adaptation to changing environmental conditions has been developed. The technique consists of 12 stages, including creating the control objects' mathematical model and tuning the controller coefficients using classical methods. The research pays special attention to the error parameters and their derivative fuzzification, which simplifies the development of logical rules and helps increase the stability of the systems. The fuzzy controller parameters were tuned using a genetic algorithm in a computational experiment based on helicopter flight data. The results show an increase in the integral quality criterion from 85.36 to 98.19%, which confirms an increase in control efficiency by 12.83%. The fuzzy controller use made it possible to significantly improve the helicopter turboshaft engines' gas-generator rotor speed control performance, reducing the first and second types of errors by 2.06 & mldr;12.58 times compared to traditional methods.
Keyword:
fuzzy controller
fuzzy logic
cognitive computing
genetic algorithm
helicopter turboshaft engine
gas-generator rotor speed
integral quality criterion
期刊
B
IF:
4.4
论文数:
1.3K
被引数:
2.4K
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