<p>This study presents a novel approach for optimizing UAV (unmanned aerial vehicle) Multicircular flight control by developing a fractional order proportional integral derivative (FOPID)-based hybrid Eagle strategy particle swarm optimization ant lion optimizer (HESPSOALO). The proposed algorithm combines the strengths of particle swarm optimization (PSO) and the ant lion optimizer (ALO), which are enhanced by the Eagle strategy to systematically fine-tune the FOPID controller parameters. This hybrid optimization method aims to improve system stability, responsiveness, and disturbance rejection in UAVs, particularly in challenging dynamic flight conditions. The proposed approach was validated against traditional control methods that utilize FOPID (Base), the Base HESPSOALO algorithm, the FOPID-based HPSOGWO (Hybrid Particle Swarm Optimization-Gray Wolf Optimizer), and the FOPID-based HGWOALO (Hybrid Gray Wolf Optimization-Ant Lion Optimizer) with a set of benchmark functions used in the analysis. The results demonstrate a minimization of position and angular errors, reduced oscillations, and overall improved control stability for the FOPID-based HESPSOALO compared with the other methods. Furthermore, a multicriteria decision-making (MCDM) framework is applied to evaluate the overall performance of alternative control strategies utilizing the CRiteria importance through intercriteria correlation (CRITIC) and technique of order preference by similarity to ideal solution (TOPSIS) techniques. The MCDM analysis demonstrates that among the evaluated criteria, <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_1508_Article_IEq1.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text{Kp}\)</EquationSource> </InlineEquation> has the highest importance, with a weight of 0.244019, whereas <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_1508_Article_IEq2.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="TEX">\(\text{Kd}\)</EquationSource> </InlineEquation> is deemed the least significant, with a weight of 0.161023. The ranking results reveal that the HESPSOALO algorithm (Base) is the best-performing controller method, with a ranking score of 0.571161, indicating its superior control performance across major metrics. In contrast, the FOPID + HPSOGWO controller method ranks the lowest, with a score of 0.282794. The findings have significant industrial implications, particularly in sectors where UAVs are critical for precision tasks, such as logistics, agriculture, surveillance, and environmental monitoring. By optimizing the FOPID controller parameters, the HESPSOALO algorithm enhances UAV stability, responsiveness, and reliability in dynamic environments, resulting in more precise control and robust performance under varying conditions. This improvement may reduce operational risks and maintenance costs while increasing efficiency, prolonging UAV service life, and achieving energy savings. This study provides a robust solution for UAV control based on the potential of hybrid optimization algorithms to improve UAV precision and reliability in autonomous flight.</p>

Multi-criteria decision model for multicircular flight control of unmanned aerial vehicles through a hybrid approach

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2025年5月30日

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Noorulden Basil,
Hamzah M. Marhoon,
Bayan Mahdi Sabbar,
Abdullah Fadhil Mohammed,
Osamah Albahri,
Ahmed Albahri,
Abdullah Alamoodi,
Iman Mohamad Sharaf,
Amare Merfo Amsal,
Mahrous Ahmed,
Enas Ali,
Sherif S. M. Ghoneim

摘要

This study presents a novel approach for optimizing UAV (unmanned aerial vehicle) Multicircular flight control by developing a fractional order proportional integral derivative (FOPID)-based hybrid Eagle strategy particle swarm optimization ant lion optimizer (HESPSOALO). The proposed algorithm combines the strengths of particle swarm optimization (PSO) and the ant lion optimizer (ALO), which are enhanced by the Eagle strategy to systematically fine-tune the FOPID controller parameters. This hybrid optimization method aims to improve system stability, responsiveness, and disturbance rejection in UAVs, particularly in challenging dynamic flight conditions. The proposed approach was validated against traditional control methods that utilize FOPID (Base), the Base HESPSOALO algorithm, the FOPID-based HPSOGWO (Hybrid Particle Swarm Optimization-Gray Wolf Optimizer), and the FOPID-based HGWOALO (Hybrid Gray Wolf Optimization-Ant Lion Optimizer) with a set of benchmark functions used in the analysis. The results demonstrate a minimization of position and angular errors, reduced oscillations, and overall improved control stability for the FOPID-based HESPSOALO compared with the other methods. Furthermore, a multicriteria decision-making (MCDM) framework is applied to evaluate the overall performance of alternative control strategies utilizing the CRiteria importance through intercriteria correlation (CRITIC) and technique of order preference by similarity to ideal solution (TOPSIS) techniques. The MCDM analysis demonstrates that among the evaluated criteria, \(\text{Kp}\) has the highest importance, with a weight of 0.244019, whereas \(\text{Kd}\) is deemed the least significant, with a weight of 0.161023. The ranking results reveal that the HESPSOALO algorithm (Base) is the best-performing controller method, with a ranking score of 0.571161, indicating its superior control performance across major metrics. In contrast, the FOPID + HPSOGWO controller method ranks the lowest, with a score of 0.282794. The findings have significant industrial implications, particularly in sectors where UAVs are critical for precision tasks, such as logistics, agriculture, surveillance, and environmental monitoring. By optimizing the FOPID controller parameters, the HESPSOALO algorithm enhances UAV stability, responsiveness, and reliability in dynamic environments, resulting in more precise control and robust performance under varying conditions. This improvement may reduce operational risks and maintenance costs while increasing efficiency, prolonging UAV service life, and achieving energy savings. This study provides a robust solution for UAV control based on the potential of hybrid optimization algorithms to improve UAV precision and reliability in autonomous flight.