Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode Control
This paper tackles the challenge of achieving robust and precise control for a novel quadrotor featuring continuously variable arm lengths (15 cm to 19 cm), enabling enhanced adaptability in complex environments. Unlike conventional fixed-geometry or discretely morphing unmanned aerial vehicles, thi...
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MDPI AG
2025-06-01
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| author | Foad Hamzeh Siavash Fathollahi Dehkordi Alireza Naeimifard Afshin Abyaz |
| author_facet | Foad Hamzeh Siavash Fathollahi Dehkordi Alireza Naeimifard Afshin Abyaz |
| author_sort | Foad Hamzeh |
| collection | DOAJ |
| description | This paper tackles the challenge of achieving robust and precise control for a novel quadrotor featuring continuously variable arm lengths (15 cm to 19 cm), enabling enhanced adaptability in complex environments. Unlike conventional fixed-geometry or discretely morphing unmanned aerial vehicles, this design’s continuous structural changes introduce significant complexities in modeling its time-varying moment of inertia. To address this, we propose a control strategy that decouples dynamic motion from the evolving geometry, allowing for the development of a robust control model. A sliding mode control algorithm, optimized using particle swarm optimization, is implemented to ensure stability and high performance in the presence of uncertainties and noise. Extensive MATLAB 2016 simulations validate the proposed approach, demonstrating superior tracking accuracy in both fixed and variable arm-length configurations, achieving root mean square error values of 0.05 m (fixed arms), 0.06 m (variable arms, path 1), and 0.03 m (variable arms, path 2). Notably, the PSO-tuned SMC controller reduces tracking error by 30% (0.07 m vs. 0.10 m for PID) and achieves a 40% faster settling time during structural transitions. This improvement is attributed to the PSO-optimized SMC parameters that effectively adapt to the continuously changing inertia, concurrently minimizing chattering by 10%. This research advances the field of morphing UAVs by integrating continuous geometric adaptability with precise and robust control, offering significant potential for energy-efficient flight and navigation in confined spaces, as well as applications in autonomous navigation and industrial inspection. |
| format | Article |
| id | doaj-art-9242f03ea0804668bce5e06d2499c8d2 |
| institution | DOAJ |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Actuators |
| spelling | doaj-art-9242f03ea0804668bce5e06d2499c8d22025-08-20T02:45:46ZengMDPI AGActuators2076-08252025-06-0114730810.3390/act14070308Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode ControlFoad Hamzeh0Siavash Fathollahi Dehkordi1Alireza Naeimifard2Afshin Abyaz3Department of Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz P.O. Box 6135783151, IranMechanical Engineering, Department of Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz P.O. Box 6135783151, IranMechanical Engineering, Department of Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz P.O. Box 6135783151, IranDepartment of Mechanical Engineering, Shahid Chamran University of Ahvaz, Ahvaz P.O. Box 6135783151, IranThis paper tackles the challenge of achieving robust and precise control for a novel quadrotor featuring continuously variable arm lengths (15 cm to 19 cm), enabling enhanced adaptability in complex environments. Unlike conventional fixed-geometry or discretely morphing unmanned aerial vehicles, this design’s continuous structural changes introduce significant complexities in modeling its time-varying moment of inertia. To address this, we propose a control strategy that decouples dynamic motion from the evolving geometry, allowing for the development of a robust control model. A sliding mode control algorithm, optimized using particle swarm optimization, is implemented to ensure stability and high performance in the presence of uncertainties and noise. Extensive MATLAB 2016 simulations validate the proposed approach, demonstrating superior tracking accuracy in both fixed and variable arm-length configurations, achieving root mean square error values of 0.05 m (fixed arms), 0.06 m (variable arms, path 1), and 0.03 m (variable arms, path 2). Notably, the PSO-tuned SMC controller reduces tracking error by 30% (0.07 m vs. 0.10 m for PID) and achieves a 40% faster settling time during structural transitions. This improvement is attributed to the PSO-optimized SMC parameters that effectively adapt to the continuously changing inertia, concurrently minimizing chattering by 10%. This research advances the field of morphing UAVs by integrating continuous geometric adaptability with precise and robust control, offering significant potential for energy-efficient flight and navigation in confined spaces, as well as applications in autonomous navigation and industrial inspection.https://www.mdpi.com/2076-0825/14/7/308variable geometric structurerobust controlsliding mode control (SMC)particle swarm optimization (PSO)morphing UAVtime-varying inertia |
| spellingShingle | Foad Hamzeh Siavash Fathollahi Dehkordi Alireza Naeimifard Afshin Abyaz Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode Control Actuators variable geometric structure robust control sliding mode control (SMC) particle swarm optimization (PSO) morphing UAV time-varying inertia |
| title | Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode Control |
| title_full | Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode Control |
| title_fullStr | Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode Control |
| title_full_unstemmed | Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode Control |
| title_short | Continuously Variable Geometry Quadrotor: Robust Control via PSO-Optimized Sliding Mode Control |
| title_sort | continuously variable geometry quadrotor robust control via pso optimized sliding mode control |
| topic | variable geometric structure robust control sliding mode control (SMC) particle swarm optimization (PSO) morphing UAV time-varying inertia |
| url | https://www.mdpi.com/2076-0825/14/7/308 |
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