{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T04:09:02Z","timestamp":1774498142011,"version":"3.50.1"},"reference-count":100,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>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\u2019 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\u2019 gas-generator rotor speed control performance, reducing the first and second types of errors by 2.06\u202612.58 times compared to traditional methods.<\/jats:p>","DOI":"10.3390\/bdcc9010017","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T07:47:37Z","timestamp":1737359257000},"page":"17","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8009-5254","authenticated-orcid":false,"given":"Serhii","family":"Vladov","sequence":"first","affiliation":[{"name":"Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"key":"ref_1","first-page":"49","article-title":"A New Intelligent Controller Based on Integral Sliding Mode Control and Extended State Observer for Nonlinear MIMO Drone Quadrotor","volume":"5","author":"Abdillah","year":"2024","journal-title":"Int. J. Intell. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.procs.2024.08.055","article-title":"Thermal Optimization Design of a Intelligent Programmable Controller Based on CFD Software","volume":"241","author":"Yang","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3181","DOI":"10.1016\/j.procs.2024.04.301","article-title":"Soft Computing Algorithm-Based Intelligent Fuzzy Controller for Enhancing the Network Stability of IPS","volume":"235","author":"Kalyan","year":"2024","journal-title":"Procedia Comput. 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