Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning
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’ m...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/9/1/17 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832589159981645824 |
|---|---|
| author | Serhii Vladov |
| author_facet | Serhii Vladov |
| author_sort | Serhii Vladov |
| collection | DOAJ |
| description | 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…12.58 times compared to traditional methods. |
| format | Article |
| id | doaj-art-fd9651066f9e4892a8dcb826e6bffcbd |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-fd9651066f9e4892a8dcb826e6bffcbd2025-01-24T13:22:34ZengMDPI AGBig Data and Cognitive Computing2504-22892025-01-01911710.3390/bdcc9010017Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for TuningSerhii Vladov0Kharkiv National University of Internal Affairs, 27, L. Landau Avenue, 61080 Kharkiv, UkraineIn 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…12.58 times compared to traditional methods.https://www.mdpi.com/2504-2289/9/1/17fuzzy controllerfuzzy logiccognitive computinggenetic algorithmhelicopter turboshaft enginegas-generator rotor speed |
| spellingShingle | Serhii Vladov Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning Big Data and Cognitive Computing fuzzy controller fuzzy logic cognitive computing genetic algorithm helicopter turboshaft engine gas-generator rotor speed |
| title | Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning |
| title_full | Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning |
| title_fullStr | Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning |
| title_full_unstemmed | Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning |
| title_short | Cognitive Method for Synthesising a Fuzzy Controller Mathematical Model Using a Genetic Algorithm for Tuning |
| title_sort | cognitive method for synthesising a fuzzy controller mathematical model using a genetic algorithm for tuning |
| topic | fuzzy controller fuzzy logic cognitive computing genetic algorithm helicopter turboshaft engine gas-generator rotor speed |
| url | https://www.mdpi.com/2504-2289/9/1/17 |
| work_keys_str_mv | AT serhiivladov cognitivemethodforsynthesisingafuzzycontrollermathematicalmodelusingageneticalgorithmfortuning |