Thursday, January 22, 2026
Space & Astronomy
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Advanced Fuzzy Logic Control for Kestrel-Inspired Ornithopters

Nature
January 19, 20263 days ago
Fuzzy logic based nonlinear blending hybrid control of a kestrel-inspired ornithopter operating in sinusoidal and dryden gusts

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This article details a fuzzy logic based nonlinear blending hybrid control system for a kestrel-inspired ornithopter. The control system is designed to manage the ornithopter's flight through sinusoidal and Dryden gusts. The key outcome is enhanced stability and control performance for the bio-inspired aerial vehicle operating in turbulent atmospheric conditions.

Liu, C. et al. Mimicking nature’s insects: A review of bio-inspired flapping-wing micro robots (FWMRs). J. Bionic. Eng. 22, 458–479. https://doi.org/10.1007/s42235-025-00648-1 (2025). Kwon, H. K. & Chang, J. W. Effects of shapes and kinematics of hovering flapping wings on aerodynamic forces and vortex structures. Sci. Rep. 15, 5098. https://doi.org/10.1038/s41598-025-86113-9 (2025). Hai, S. et al. PENC: A predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments. Sci. Rep. 15, 29753. https://doi.org/10.1038/s41598-025-13095-z (2025). Lu, X. et al. An insect-scale flapping-wing micro aerial vehicle inspired by tumblers capable of uncontrolled self-stabilizing flying. Research 8, 0787. https://doi.org/10.34133/research.0787 (2025). Salih, A. R., Ibrahim, M. O. O. & Aziz, J. S. B. M. Effect of synthetic jet actuators on UAV aerodynamic performance. IEEE Access 8, 156245–156257. https://doi.org/10.1109/ACCESS.2020.3011139 (2020). Wang, S., Li, Y. & He, W. Flight attitude estimation for MAV based on M-estimation. In Proceedings of the International Conference on Consumer Electronics, Communications and Networks (CECNet) (2011). De Rosa, D., Fernandes, S. G. & Pereira, M. M. Vortex generators for load alleviation in gusty flight conditions. AIAA J. 63, 1121–1135. https://doi.org/10.2514/6.2025-1069 (2025). Tejaswi, K. C., Kang, C.-K. & Lee, T. Dynamics and control of a flapping wing UAV with abdomen undulation inspired by monarch butterfly. In Proceedings of the American Control Conference (ACC) 66–71. https://doi.org/10.23919/ACC50511.2021.9483293 (2021). Luo, B., Cui, W. & Li, W. Twisting morphing wings with tight geometric constraints for biomimetic swimming or flying robotic vehicles. J. Eng. Res. 13, 797–807. https://doi.org/10.1016/j.jer.2023.10.036 (2025). Xin, T. & Li, B. Research on aerodynamic characteristics and control method of rigid-flexible variable camber wing. Sci. Rep. 15, 25904. https://doi.org/10.1038/s41598-025-08792-8 (2025). Xu, X. Z. Fractional-order fast terminal sliding mode control of a nonlinear aeroelastic wing section considering gust load. Sci. Rep. 15, 21789. https://doi.org/10.1038/s41598-025-06503-x (2025). Tunca, S. G., Özgür, M. A. & Koşar, O. Experimental investigation of the aerodynamic performance of Rhamphorhynchus muensteri-inspired airfoil profiles at low Reynolds numbers. J. Eng. Res. https://doi.org/10.1016/j.jer.2025.08.005 (2025). Kim, T. et al. Wing-strain-based flight control of flapping-wing drones through reinforcement learning. Nat. Mach. Intell. 6, 992–1005. https://doi.org/10.1038/s42256-024-00893-9 (2024). Ünal, N., Öz, Y., Ünal, E. A. & Oktay, T. Enhancing aerodynamic performance of a two-dimensional airfoil using plasma actuators. Aerosp. Sci. Technol. 158, 109882. https://doi.org/10.1016/j.ast.2024.109882 (2025). Kownacki, C., Romaniuk, S. & Derlatka, M. Applying neural networks as direct controllers in position and trajectory tracking algorithms for holonomic UAVs. Sci. Rep. 15, 12605. https://doi.org/10.1038/s41598-025-97215-9 (2025). Metin, U. & Oktay, T. Simultaneous UAV having actively sweep angle morphing wing and flight control system design. Aircraft Eng. Aerosp. Technol. 95, 1062–1068. https://doi.org/10.1108/AEAT-09-2022-0259 (2023). Admas, Y. A. et al. Control of a fixed wing unmanned aerial vehicle using a higher-order sliding mode controller and non-linear PID controller. Sci. Rep. 14, 23139. https://doi.org/10.1038/s41598-024-73901-y (2024). Sal, F. Effects of the actively morphing root chord and taper on helicopter energy. Aircraft Eng. Aerosp. Technol. 92, 264–270. https://doi.org/10.1108/AEAT-08-2019-0165 (2020). Yu, Y., Lu, Q. & Zhang, B. Reinforcement learning based recovery flight control for flapping-wing micro-aerial vehicles under extreme attitudes. Int. J. Adv. Robot. Syst. https://doi.org/10.1177/17298806241303290 (2025). Zheng, H., Chen, W. & Xie, F. Control simulation of flapping wing micro aerial vehicle based on multi-level optimization model predictive control. IEEE Access 12, 40700–40709. https://doi.org/10.1109/ACCESS.2024.3376646 (2024). Dhingra, D., Kaheman, K. & Fuller, S. B. Modeling and LQR control of insect-sized flapping wing robot. npj Robotics 3, 6. https://doi.org/10.1038/s44182-025-00022-7 (2025). Bhatia, M., Patil, M. & Woolsey, C. Stabilization of flapping-wing micro-air vehicles in gust environments. J. Guid. Control Dyn. 37, 367–376 (2014). Coleman, D. & Benedict, M. Methods for stabilizing the longitudinal dynamics of a biomimetic robotic hummingbird in hovering flight. Int. J. Micro Air Veh. 17, 1–17. https://doi.org/10.1177/17568293251363587 (2025). Zheng, Z., Wu, L., Chen, Y., Li, S. & Dong, X. Tailless flapping-wing robot with bio-inspired elastic passive legs for multi-modal locomotion. IEEE Robot. Autom. Lett. 10, 13051–13058. https://doi.org/10.1109/LRA.2025.3303645 (2025). Fang, X. et al. Review of the flight control method of a bird-like flapping-wing air vehicle. Micromachines 14, 1547. https://doi.org/10.3390/mi14081547 (2023). Bechert, D. W., Bruse, M., Hage, W. & Meyer, R. Biological surfaces and their technological application—laboratory and flight experiments on drag reduction and separation control. In Proceedings of the 28th AIAA Fluid Dynamics Conference. Paper AIAA-1997–1960. https://doi.org/10.2514/6.1997-1960 (1997). Abbasi, S. H. & Mahmood, A. Bio-inspired gust mitigation system for a flapping wing UAV: Modeling and simulation. J. Braz. Soc. Mech. Sci. Eng. 41, 524. https://doi.org/10.1007/s40430-019-2044-9 (2019). Abbasi, S. H., Mahmood, A. & Khaliq, A. Multi-body bond graph modeling and simulation of a bio-inspired gust mitigating flapping wing UAV. Songklanakarin J. Sci. Technol. 44, 1238–1247 (2022). Abbasi, S. H. et al. Reduced order modeling and simulation of a bio-inspired gust mitigating flapping wing UAV. Int. J. Intell. Robot. Appl. 6, 587–601. https://doi.org/10.1007/s41315-022-00247-x (2022). Abbasi, S. H. et al. LQR controller for stabilization of bio-inspired flapping wing UAV in gust environments. J. Intell. Robot. Syst. 105, 79. https://doi.org/10.1007/s10846-022-01699-w (2022). Abbasi, S. H., Shamsan, Z. A. & Abbasi, N. Robust H∞ control synthesis of a biomimetic flapping wing UAV operating in gusty environment. IEEE Access 13, 98990–99002. https://doi.org/10.1109/ACCESS.2025.3576369 (2025). Send, W. et al. Artificial hinged-wing bird with active torsion and partially linear kinematics. In Proceedings of the 28th International Congress of the Aeronautical Sciences (ICAS) (2012). Karnopp, D. C., Margolis, D. L. & Rosenberg, R. C. System dynamics: Modeling and simulation of mechatronic systems 4th edn. (Wiley, 2000). Samar, R., Postlethwaite, I. & Gu, D. W. Model reduction with balanced realizations. Int. J. Control 62, 33–64. https://doi.org/10.1080/00207179508921533 (1995). Anderson, B. D. & Moore, J. B. Optimal control: Linear quadratic methods (Prentice Hall, 1990). Zhou, K. & Doyle, J. C. Essentials of Robust Control. Prentice Hall, Englewood Cliffs, NJ, 26–290 (1998). Jahanbin, Z., Ghafari, A. S., Ebrahimi, A. & Meghdari, A. Multibody simulation of a flapping-wing robot using an efficient dynamical model. J. Braz. Soc. Mech. Sci. Eng. 38, 133–149. https://doi.org/10.1007/s40430-015-0350-4 (2016).

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    Ornithopter Control: Fuzzy Logic Hybrid System