Enhancing Intelligent Control Strategies for UAVs: A Comparative Analysis of Fuzzy Logic, Fuzzy PID, and GA-Optimized Fuzzy PID Controllers
This paper presents advanced control strategies to enhance the stability and trajectory tracking performance of quadrotor systems. The study investigates three control methodologies: the Fuzzy Logic Controller (Fuzzy), the Fuzzy Proportional-Integral-Derivative (FPID) controller, and the Genetic Alg...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10849535/ |
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| author | Nigatu Wanore Madebo |
| author_facet | Nigatu Wanore Madebo |
| author_sort | Nigatu Wanore Madebo |
| collection | DOAJ |
| description | This paper presents advanced control strategies to enhance the stability and trajectory tracking performance of quadrotor systems. The study investigates three control methodologies: the Fuzzy Logic Controller (Fuzzy), the Fuzzy Proportional-Integral-Derivative (FPID) controller, and the Genetic Algorithm (GA)-optimized Fuzzy PID controller (GAFPID). The Fuzzy controller leverages heuristic rules for adaptive control, while the FPID controller integrates conventional PID dynamics with fuzzy logic to improve precision and robustness. The GAFPID controller employs evolutionary computation through a genetic algorithm to optimize parameter tuning, offering superior control performance. Comparative simulations are conducted under diverse operating conditions, including external disturbances and parameter variation scenarios, with performance evaluated using the Integral of Time-weighted Absolute Error (ITAE) metric. Results demonstrate that the GAFPID controller outperforms the other approaches in terms of precision, adaptability, and robustness, establishing it as a promising solution for complex quadrotor applications. |
| format | Article |
| id | doaj-art-8f9db181313c43f4bc63169e239ab2e1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8f9db181313c43f4bc63169e239ab2e12025-01-29T00:00:49ZengIEEEIEEE Access2169-35362025-01-0113165481656310.1109/ACCESS.2025.353274310849535Enhancing Intelligent Control Strategies for UAVs: A Comparative Analysis of Fuzzy Logic, Fuzzy PID, and GA-Optimized Fuzzy PID ControllersNigatu Wanore Madebo0https://orcid.org/0009-0001-3941-7319Information Network Security Administration (INSA), Addis Ababa, EthiopiaThis paper presents advanced control strategies to enhance the stability and trajectory tracking performance of quadrotor systems. The study investigates three control methodologies: the Fuzzy Logic Controller (Fuzzy), the Fuzzy Proportional-Integral-Derivative (FPID) controller, and the Genetic Algorithm (GA)-optimized Fuzzy PID controller (GAFPID). The Fuzzy controller leverages heuristic rules for adaptive control, while the FPID controller integrates conventional PID dynamics with fuzzy logic to improve precision and robustness. The GAFPID controller employs evolutionary computation through a genetic algorithm to optimize parameter tuning, offering superior control performance. Comparative simulations are conducted under diverse operating conditions, including external disturbances and parameter variation scenarios, with performance evaluated using the Integral of Time-weighted Absolute Error (ITAE) metric. Results demonstrate that the GAFPID controller outperforms the other approaches in terms of precision, adaptability, and robustness, establishing it as a promising solution for complex quadrotor applications.https://ieeexplore.ieee.org/document/10849535/FPIDGAFPIDfuzzyunmanned aerial vehicle (UAV) |
| spellingShingle | Nigatu Wanore Madebo Enhancing Intelligent Control Strategies for UAVs: A Comparative Analysis of Fuzzy Logic, Fuzzy PID, and GA-Optimized Fuzzy PID Controllers IEEE Access FPID GAFPID fuzzy unmanned aerial vehicle (UAV) |
| title | Enhancing Intelligent Control Strategies for UAVs: A Comparative Analysis of Fuzzy Logic, Fuzzy PID, and GA-Optimized Fuzzy PID Controllers |
| title_full | Enhancing Intelligent Control Strategies for UAVs: A Comparative Analysis of Fuzzy Logic, Fuzzy PID, and GA-Optimized Fuzzy PID Controllers |
| title_fullStr | Enhancing Intelligent Control Strategies for UAVs: A Comparative Analysis of Fuzzy Logic, Fuzzy PID, and GA-Optimized Fuzzy PID Controllers |
| title_full_unstemmed | Enhancing Intelligent Control Strategies for UAVs: A Comparative Analysis of Fuzzy Logic, Fuzzy PID, and GA-Optimized Fuzzy PID Controllers |
| title_short | Enhancing Intelligent Control Strategies for UAVs: A Comparative Analysis of Fuzzy Logic, Fuzzy PID, and GA-Optimized Fuzzy PID Controllers |
| title_sort | enhancing intelligent control strategies for uavs a comparative analysis of fuzzy logic fuzzy pid and ga optimized fuzzy pid controllers |
| topic | FPID GAFPID fuzzy unmanned aerial vehicle (UAV) |
| url | https://ieeexplore.ieee.org/document/10849535/ |
| work_keys_str_mv | AT nigatuwanoremadebo enhancingintelligentcontrolstrategiesforuavsacomparativeanalysisoffuzzylogicfuzzypidandgaoptimizedfuzzypidcontrollers |