Adaptive maneuvering control of a quadrotor UAV using double dynamic filters

Cun Yang; Cun Yang

doi:10.3934/era.2026217

Electronic Research Archive

2026, Volume 34, Issue 7: 4913-4930. doi: 10.3934/era.2026217

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Research article

Adaptive maneuvering control of a quadrotor UAV using double dynamic filters

Cun Yang ^,

School of Mathematics and Informational Science, Shandong Technology and Business University, Yantai 264005, China

Received: 22 March 2026 Revised: 11 May 2026 Accepted: 01 June 2026 Published: 08 June 2026

This paper proposes an adaptive maneuvering control for a quadrotor unmanned aerial vehicle with position constrained by geometric equations and unknown parameters. Based on the structural characteristics of the quadrotors, the inner–outer-loop controller is designed for two cascaded subsystems to solve the maneuvering problem of the position subsystem and the Euler angle tracking problem of the attitude subsystem. Meanwhile, double dynamic filters are incorporated into the the inner-loop control to eliminate the coefficient explosion problem of virtual signal differentiation. By treating the coupling term as a perturbation, the semi-global practical stability of the closed-loop system is established. Finally, the effectiveness of the obtained results are demonstrated by numerical simulations.
- unmanned aerial vehicle,
- maneuvering control,
- double dynamic filter
Citation: Cun Yang. Adaptive maneuvering control of a quadrotor UAV using double dynamic filters[J]. Electronic Research Archive, 2026, 34(7): 4913-4930. doi: 10.3934/era.2026217

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Abstract

This paper proposes an adaptive maneuvering control for a quadrotor unmanned aerial vehicle with position constrained by geometric equations and unknown parameters. Based on the structural characteristics of the quadrotors, the inner–outer-loop controller is designed for two cascaded subsystems to solve the maneuvering problem of the position subsystem and the Euler angle tracking problem of the attitude subsystem. Meanwhile, double dynamic filters are incorporated into the the inner-loop control to eliminate the coefficient explosion problem of virtual signal differentiation. By treating the coupling term as a perturbation, the semi-global practical stability of the closed-loop system is established. Finally, the effectiveness of the obtained results are demonstrated by numerical simulations.

References

[1]	L. N. Zheng, Z. J. Zhang, Convergence and robustness analysis of novel adaptive multilayer neural dynamics-based controllers of multirotor UAVs, IEEE Trans. Cybern., 51 (2021), 3710–3723. https://doi.org/10.1109/TCYB.2019.2923642 doi: 10.1109/TCYB.2019.2923642
[2]	L. N. Zheng, F. Q. Deng, Z. L. Yu, Y. M. Luo, Z. J. Zhang, Multilayer neural dynamics-based adaptive control of multirotor UAVs for tracking time-varying tasks, IEEE Trans. Syst. Man Cybern. Syst., 52 (2022), 5889–5900. https://doi.org/10.1109/TSMC.2021.3130748 doi: 10.1109/TSMC.2021.3130748
[3]	J. Olguin-Roque, S. Salazar, I. González Hernandez, R. Lozano, A robust fixed-time sliding mode control for quadrotor UAV, Algorithms, 16 (2023), 229. https://doi.org/10.3390/a16050229 doi: 10.3390/a16050229
[4]	M. Khodaverdian, S. Hajshirmohamadi, A. Hakobyan, S. Ijaz, Predictor-based constrained fixed-time sliding mode control of multi-UAV formation flight, Aerosp. Sci. Technol., 148 (2024), 109113. https://doi.org/10.1016/j.ast.2024.109113 doi: 10.1016/j.ast.2024.109113
[5]	H. Z. Guo, M. Chen, Y. H. Shen, M. Lungu, Distributed event-triggered collision avoidance formation control for QUAVs with disturbances based on virtual tubes, IEEE Trans. Ind. Electron., 72 (2025), 1892–1903. https://doi.org/10.1109/TIE.2024.3417975 doi: 10.1109/TIE.2024.3417975
[6]	X. B. Lin, J. Liu, Y. Yu, C. Y. Sun, Event-triggered reinforcement learning control for the quadrotor UAV with actuator saturation, Neurocomputing, 415 (2020), 135–145. https://doi.org/10.1016/j.neucom.2020.07.042 doi: 10.1016/j.neucom.2020.07.042
[7]	F. Hu, T. D. Ma, X. J. Su, Adaptive fuzzy sliding-mode fixed-time control for quadrotor unmanned aerial vehicles with prescribed performance, IEEE Trans. Fuzzy Syst., 32 (2024), 4109–4120. https://doi.org/10.1109/TFUZZ.2024.3393763 doi: 10.1109/TFUZZ.2024.3393763
[8]	L. F. Hua, W. H. Wang, K. B. Shi, J. Yang, Q. S. Zhong, H. J. Liang, Safety-critical fixed-time trajectory tracking of quadrotor UAVs via event-triggered prescribed performance sliding mode control, IEEE Trans. Consum. Electron., 72 (2026), 1821–1830. https://doi.org/10.1109/TCE.2025.3650623 doi: 10.1109/TCE.2025.3650623
[9]	K. Liu, L. Jiao, Y. Zhang, P. Y. Zhao, Adaptive predefined-time prescribed performance control with application to a quadrotor UAV, IEEE Trans. Aerosp. Electron. Syst., 62 (2026), 4751–4770. https://doi.org/10.1109/TAES.2026.3654056 doi: 10.1109/TAES.2026.3654056
[10]	O. Mofid, S. Mobayen, Robust fractional-order sliding mode tracker for quad-rotor UAVs: event-triggered adaptive backstepping approach under disturbance and uncertainty, Aerosp. Sci. Technol., 146 (2024), 108916. https://doi.org/10.1016/j.ast.2024.108916 doi: 10.1016/j.ast.2024.108916
[11]	J. Baek, M. Kang, A synthesized sliding-mode control for attitude trajectory tracking of quadrotor UAV systems, IEEE/ASME Trans. Mechatron., 28 (2023), 2189–2199. https://doi.org/10.1109/TMECH.2022.3230755 doi: 10.1109/TMECH.2022.3230755
[12]	M. D. Golea, M. Lungu, R. Wang, D. G. Voinea, High-order disturbance observer-based backstepping sliding mode control of the under-actuated uncertain quadrotor unmanned aerial vehicles, in 2025 29th International Conference on System Theory, Control and Computing (ICSTCC), (2025), 679–684. https://doi.org/10.1109/ICSTCC66753.2025.11240397
[13]	L. X. Xu, H. J. Ma, D. Guo, A. H. Xie, D. L. Song, Backstepping sliding-mode and cascade active disturbance rejection control for a quadrotor UAV, IEEE/ASME Trans. Mechatron., 25 (2020), 2743–2753. https://doi.org/10.1109/TMECH.2020.2990582 doi: 10.1109/TMECH.2020.2990582
[14]	Z. J. Wang, T. Zhao, Based on robust sliding mode and linear active disturbance rejection control for attitude of quadrotor load UAV, Nonlinear Dyn., 108 (2022), 3485–3503. https://doi.org/10.1007/s11071-022-07349-y doi: 10.1007/s11071-022-07349-y
[15]	H. Liu, Y. F. Lyu, W. B. Zhao, Robust visual servoing formation tracking control for quadrotor UAV team, Aerosp. Sci. Technol., 106 (2020), 106061. https://doi.org/10.1016/j.ast.2020.106061 doi: 10.1016/j.ast.2020.106061
[16]	H. Z. Guo, M. Chen, B. M. Li, Distributed event-triggered collision avoidance coordinated control for QUAVs based on flexible virtual tubes, Chin. J. Aeronaut., 38 (2025), 103300. https://doi.org/10.1016/j.cja.2024.11.010 doi: 10.1016/j.cja.2024.11.010
[17]	M. Jabeen, Q. H. Meng, H. R. Hou, H. Y. Li, Odor source localization in outdoor building environments through distributed cooperative control of a fleetof UAVs, Expert Syst. Appl., 247 (2024), 123332. https://doi.org/10.1016/j.eswa.2024.123332 doi: 10.1016/j.eswa.2024.123332
[18]	K. Liu, W. Y. Yang, L. Jiao, Z. P. Yuan, C. Y. Wen, Fast fixed-time distributed neural formation control-based disturbance observer for multiple quadrotor UAVs under unknown disturbances, IEEE Trans. Aerosp. Electron. Syst., 61 (2025), 13137–13155. https://doi.org/10.1109/TAES.2025.3578046 doi: 10.1109/TAES.2025.3578046
[19]	R. Skjetne, T. I. Fossen, P. V. Kokotović, Robust output maneuvering for a class of nonlinear systems, Automatica, 40 (2004), 373–383. https://doi.org/10.1016/j.automatica.2003.10.010 doi: 10.1016/j.automatica.2003.10.010
[20]	R. Skjetne, T. I. Fossen, P. V. Kokotović, Adaptive maneuvering, with experiments, for a model ship in a marine control laboratory, Automatica, 41 (2005), 289–298. https://doi.org/10.1016/j.automatica.2004.10.006 doi: 10.1016/j.automatica.2004.10.006
[21]	D. Cabecinhas, R. Cunha, C. Silvestre, A globally stabilizing path following controller for rotorcraft with wind disturbance rejection, IEEE Trans. Control Syst. Technol., 23 (2015), 708–714. https://doi.org/10.1109/TCST.2014.2326820 doi: 10.1109/TCST.2014.2326820
[22]	S. Z. Wu, X. Liang, Y. C. Fang, W. He, Geometric maneuvering for underactuated VTOL vehicles, IEEE Trans. Autom. Control., 69 (2024), 1507–1519. https://doi.org/10.1109/TAC.2023.3324268 doi: 10.1109/TAC.2023.3324268
[23]	F. Kendoul, Nonlinear hierarchical flight controller for unmanned rotorcraft: design, stability, and experiments, J. Guid. Control Dyn., 32 (2009), 1954–1958. https://doi.org/10.2514/1.43768 doi: 10.2514/1.43768
[24]	R. Naldi, M. Furci, R. G. Sanfelice, L. Marconi, Robust global trajectory tracking for underactuated VTOL aerial vehicles using inner-outer loop control paradigms, IEEE Trans. Autom. Control, 62 (2016), 97–112. https://doi.org/10.1109/TAC.2016.2557967 doi: 10.1109/TAC.2016.2557967
[25]	L. Martins, C. Cardeira, P. Oliveira, Global trajectory tracking for quadrotors: An MRP-based hybrid strategy with input saturation, Automatica, 162 (2024), 111521. https://doi.org/10.1016/j.automatica.2024.111521 doi: 10.1016/j.automatica.2024.111521
[26]	M. Micu, M. Lungu, M. Chen, M. R. Ebrahimpour, Four-stage cascaded control scheme based on robust nonlinear dynamic inversion technique for quadrotors, in 2024 28th International Conference on System Theory, Control and Computing (ICSTCC), (2024), 235–240. https://doi.org/10.1109/ICSTCC62912.2024.10744662
[27]	M. R. Mokhtari, B. Cherki, A. C. Braham, Disturbance observer based hierarchical control of coaxial-rotor UAV, ISA Trans., 67 (2017), 466–475. https://doi.org/10.1016/j.isatra.2017.01.020 doi: 10.1016/j.isatra.2017.01.020
[28]	J. A. Farrell, M. Polycarpou, M. Sharma, W. J. Dong, Command filtered backstepping, IEEE Trans. Autom. Control, 54 (2009), 1391–1395. https://doi.org/10.1109/TAC.2009.2015562 doi: 10.1109/TAC.2009.2015562
[29]	P. Castillo, R. Lozano, A. E. Dzul, Modelling and Control of Mini-Flying Machines, Springer-Verlag, 2005. https://doi.org/10.1007/1-84628-179-2
[30]	G. Cai, B. M. Chen, T. H. Lee, Unmanned Rotorcraft Systems, Springer Science & Business Media, London, 2011. https://doi.org/10.1007/978-0-85729-635-1
[31]	K. Yan, M. Chen, Q. X. Wu, B. Jiang, Extended state observer-based sliding mode fault-tolerant control for unmanned autonomous helicopter with wind gusts, IET Control Theory Appl., 13 (2019), 1500–1513. https://doi.org/10.1049/iet-cta.2018.5341 doi: 10.1049/iet-cta.2018.5341
[32]	K. Yan, M. Chen, Q. X. Wu, R. G. Zhu, Robust adaptive compensation control for unmanned autonomous helicopter with input saturation and actuator faults, Chin. J. Aeronaut., 32 (2019), 2299–2310. https://doi.org/10.1016/j.cja.2019.06.001 doi: 10.1016/j.cja.2019.06.001
[33]	C. Yang, Z. J. Wu, Adaptive robust maneuvering control for nonlinear systems via dynamic surface technique, Nonlinear Dyn., 111 (2023), 8369–8381. https://doi.org/10.1007/s11071-023-08289-x doi: 10.1007/s11071-023-08289-x
[34]	R. Rashad, A. Aboudonia, A. El-Badawy, A novel disturbance observer-based backstepping controller with command filtered compensation for a MIMO system, J. Franklin Inst., 353 (2016), 4039–4061. https://doi.org/10.1016/j.jfranklin.2016.07.017 doi: 10.1016/j.jfranklin.2016.07.017

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