Hostname: page-component-54dcc4c588-2bdfx Total loading time: 0 Render date: 2025-10-01T22:49:15.638Z Has data issue: false hasContentIssue false
  • English
  • Français

Robust control and optimal allocation for vector quadrotor based on model compensation control

Published online by Cambridge University Press:  01 October 2025

Liang Zhang Open the ORCID record for Liang Zhang [Opens in a new window] ,
Wenchao Du Open the ORCID record for Wenchao Du [Opens in a new window]  and
Yan Gao
Liang Zhang*
Affiliation:
The School of Control Science and Engineering, Tiangong University, Tianjin300387, China, hinata981026@163.com, https://kz.tiangong.edu.cn/main.htm
Wenchao Du
Affiliation:
The School of Control Science and Engineering, Tiangong University, Tianjin300387, China, hinata981026@163.com, https://kz.tiangong.edu.cn/main.htm
Yan Gao
Affiliation:
The School of Control Science and Engineering, Tiangong University, Tianjin300387, China, hinata981026@163.com, https://kz.tiangong.edu.cn/main.htm
*
Corresponding author: Liang Zhang; Email: liangzhang@tiangong.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Standard quadrotors exhibit limited mobility due to inherent underactuation: they only have four independent control inputs, whereas their position and attitude in space are defined by six degrees of freedom (DOF). Consequently, a quadrotor’s pose cannot track an arbitrary trajectory over time. To address this limitation, a novel actuation concept has been proposed, wherein the quadrotor’s propellers can tilt around their axes relative to the main body–forming a vector quadrotor. To achieve more accurate trajectory tracking tailored to the specific characteristics of this vector quadrotor model, we propose a novel control strategy. First, we integrate special orthogonal group SO(3) theory with model compensation control: SO(3) theory enables accurate modeling of the aircraft’s rotational dynamics, while model compensation control mitigates unmodeled dynamics and external disturbances, thereby ensuring robustness across diverse operating conditions. Second, we introduce the sequential quadratic programming (SQP) method for control allocation; this method not only enables efficient computation of control inputs but also optimises the allocation of control resources, which enhances system performance–particularly in complex manoeuvering scenarios. Finally, we integrate the SO(3)-based controller with the SQP-based control allocation module to form a unified control system. The effectiveness of this proposed approach is validated via simulation results. These results demonstrate improved trajectory tracking accuracy and enhanced robustness against disturbances, thus confirming the potential of our method for practical applications.

Keywords

vector quadrotormodel compensation controlallocation optimisationdisturbance rejection

Information

Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 25
Check for updates
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Song, F., Li, Z., Yang, S. and Rodriguez-Andina, J.J. Anti-disturbance compensation for quadrotor close crossing flight based on deep reinforcement learning, IEEE Trans. Ind. Electron., 2022, 70, (3), pp 3013–3023.10.1109/TIE.2022.3172764CrossRefGoogle Scholar
Liu, B., Li, J., Yang, Y. and Zhou, Z. Controller design for quad-rotor UAV based on variable aggregation model predictive control, Flight Cont. Detect., 2021, 4, (3), pp 1–7.Google Scholar
Hao, W., Xian, B. and Xie, T. Fault-tolerant position tracking control design for a tilt tri-rotor unmanned aerial vehicle, IEEE Trans. Ind. Electron., 2021, 69, (1), pp 604–612.10.1109/TIE.2021.3050384CrossRefGoogle Scholar
Lv, Z.Y., Wu, Y., Zhao, Q. and Sun, X.M. Design and control of a novel coaxial tilt-rotor UAV, IEEE Trans. Ind. Electron., 2021, 69, (4), pp 3810–3821.10.1109/TIE.2021.3075886CrossRefGoogle Scholar
Nguyen, H.N., Park, S., Park, J. and Lee, D. A novel robotic platform for aerial manipulation using quadrotors as rotating thrust generators, IEEE Trans. Robot., 2018, 34, (2), pp 353–369.10.1109/TRO.2018.2791604CrossRefGoogle Scholar
Hamandi, M., Usai, F., Sablé, Q., Staub, N., Tognon, M. and Franchi, A. Design of multirotor aerial vehicles: A taxonomy based on input allocation, Int. J. Robot. Res., 2021, 40, (8–9), pp 1015–1044.10.1177/02783649211025998CrossRefGoogle Scholar
Rajappa, S., Ryll, M., Bülthoff, H.H. and Franchi, A. Modeling, control and design optimization for a fully-actuated hexarotor aerial vehicle with tilted propellers, In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp 4006–4013, 2015.10.1109/ICRA.2015.7139759CrossRefGoogle Scholar
Alawadhi, A., Curiel, O., Gerber, M.J. and Tsao, T.-C. Simulation and initial experiment of a twist-tilt quadcopter for fully actuated motion, IFAC-PapersOnLine, 2023, 56, (2), pp 6292–6297.10.1016/j.ifacol.2023.10.783CrossRefGoogle Scholar
Ryll, M., Bicego, D., Giurato, M., Lovera, M. and Franchi, A. Fast-hex—A morphing hexarotor: Design, mechanical implementation, control and experimental validation, IEEE/ASME Trans. Mechatron., 2021, 27, (3), pp 1244–1255.10.1109/TMECH.2021.3099197CrossRefGoogle Scholar
Rashad, R., Engelen, J.B.C. and Stramigioli, S. Energy tank-based wrench/impedance control of a fully-actuated Hexarotor: A geometric port-Hamiltonian approach, In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp 6418–6424, 2019.10.1109/ICRA.2019.8793939CrossRefGoogle Scholar
Tognon, M. and Franchi, A. Omnidirectional aerial vehicles with unidirectional thrusters: Theory, optimal design, and control, IEEE Robot. Autom. Lett., 2018, 3, (3), pp 2277–2282.10.1109/LRA.2018.2802544CrossRefGoogle Scholar
Jacquet, M., Corsini, G., Bicego, D. and Franchi, A. Perception-constrained and motor-level nonlinear MPC for both underactuated and tilted-propeller UAVs, In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 4301–4306, 2020.Google Scholar
Tal, E. and Karaman, S. Accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness, IEEE Trans. Control Syst. Technol., 2020, 29, (3), pp 1203–1218.10.1109/TCST.2020.3001117CrossRefGoogle Scholar
Manzoor, T., Xia, Y., Zhai, D.H. and Ma, D. Trajectory tracking control of a VTOL unmanned aerial vehicle using offset-free tracking MPC. Chin. J. Aeronaut., 2020, 33, (7), pp 2024–2042.10.1016/j.cja.2020.03.003CrossRefGoogle Scholar
Tian, B., Liu, L., Lu, H., Zuo, Z., Zong, Q. and Zhang, Y. Multivariable finite time attitude control for quadrotor UAV: Theory and experimentation, IEEE Trans. Ind. Electron., 2017, 65, (3), pp 2567–2577.10.1109/TIE.2017.2739700CrossRefGoogle Scholar
Doukhi, O. and Lee, D.J. Neural network-based robust adaptive certainty equivalent controller for quadrotor UAV with unknown disturbances, Int. J. Control Autom. Syst., 2019, 17(9), pp 2365–2374.10.1007/s12555-018-0720-7CrossRefGoogle Scholar
Abdo, Ö., Erkin, T. and Çelik, H. Otonom Kol Uçuşu İçin Kontrol Sistemi Tasarımı control system design for autonomous formation flight, In Proceedings of 2021 29th Signal Processing and Communications Applications Conference (SIU), pp 1–4, 2021.10.1109/SIU53274.2021.9477915CrossRefGoogle Scholar
Nisar, B., Foehn, P., Falanga, D. and Scaramuzza, D. VIMO: Simultaneous visual inertial model-based odometry and force estimation, IEEE Robot. Autom. Lett., 2019, 4, (3), pp 2785–2792.10.1109/LRA.2019.2918689CrossRefGoogle Scholar
Zhang, Y., Chen, Z., Zhang, X., Sun, Q. and Sun, M. A novel control scheme for quadrotor UAV based upon active disturbance rejection control, Aerosp. Sci. Technol., 2018, 79, pp 601–609.10.1016/j.ast.2018.06.017CrossRefGoogle Scholar
Wang, Z. and Zhao, T. Adaptive-based linear active disturbance rejection attitude control for quadrotor with external disturbances, Trans. Inst. Meas. Control, 2022, 44, (2), pp 286–298.10.1177/01423312211031781CrossRefGoogle Scholar
He, T. and Wu, Z. Extended disturbance observer with measurement noise reduction for spacecraft attitude stabilization, IEEE Access, 2019, 7, pp 66137–66147.10.1109/ACCESS.2019.2918076CrossRefGoogle Scholar
Xia, L., Yang, L., Li, W., Wang, W. and Zhang, J. Discrete bi-bandwidth extended state observer for systems with measurement noise, IEEE Trans. Ind. Electron., 2023, 71, (7), pp 7796–7805.10.1109/TIE.2023.3314905CrossRefGoogle Scholar
Ahmad, S. and Ali, A. On active disturbance rejection control in presence of measurement noise, IEEE Trans. Ind. Electron., 2021, 69, (11), pp 11600–11610.10.1109/TIE.2021.3121754CrossRefGoogle Scholar
Qi, G., Li, X. and Chen, Z. Problems of extended state observer and proposal of compensation function observer for unknown model and application in UAV, IEEE Trans. Syst. Man Cybern. Syst., 2021, 52, (5), pp 2899–2910.10.1109/TSMC.2021.3054790CrossRefGoogle Scholar
Qi, G., Hu, J., Li, L. and Li, K. Integral compensation function observer and its application to disturbance-rejection control of QUAV attitude, IEEE Trans. Cybern., 2024, 54, (7), pp 4088–4099.10.1109/TCYB.2023.3344217CrossRefGoogle ScholarPubMed
Qi, G., Deng, J., Li, X. and Yu, X. Compensation function observer-based model-compensation backstepping control and application in anti-inference of quadrotor UAV, Control Eng. Pract., 2023, 140, p 105633.10.1016/j.conengprac.2023.105633CrossRefGoogle Scholar
Li, X., Qi, G., Guo, X., Chen, Z. and Zhao, X. Improved high order differential feedback control of quadrotor UAV based on improved extended state observer, J. Franklin Inst., 2022, 359, (9), pp 4233–4259.10.1016/j.jfranklin.2022.03.019CrossRefGoogle Scholar
Yih, C.C. and Wu, S.J. Sliding mode path following and control allocation of a tilt-rotor quadcopter, Appl. Sci., 2022, 12, (21), p 11088.10.3390/app122111088CrossRefGoogle Scholar
Yang, Y., Yu, X., Li, Z. and Basin, M.V. A new overactuated multirotor: Prototype design, dynamics modeling, and control, IEEE Trans. Ind. Electron., 2023, 71, (8), pp 9449–9459.10.1109/TIE.2023.3314924CrossRefGoogle Scholar
Wang, Y., You, X. and Baghdadi, M. Non-linear model predictive control of a tilt-rotor quadcopter for control allocation and path following, In Proc. Int. Conf. Global Aeronaut. Eng. Satell. Technol. (GAST), pp 1–6, 2024.10.1109/GAST60528.2024.10520761CrossRefGoogle Scholar
Aslan, S. and Erkin, T. An immune plasma algorithm based approach for UCAV path planning, Journal of King Saud University-Computer and Information Sciences, 2023, 35, (1), pp 56–69.10.1016/j.jksuci.2022.06.004CrossRefGoogle Scholar
Aslan, S. and Erkin, T. A multi-population immune plasma algorithm for path planning of unmanned combat aerial vehicle, Adv. Eng. Inform., 2023, 55, p 101829.10.1016/j.aei.2022.101829CrossRefGoogle Scholar
Aslan, S. and Erkin, T. DUALIPA: A new immune plasma algorithm for path planning of unmanned aerial vehicles, Clust. Comput., 2025, 28, (4), p 259.10.1007/s10586-024-04941-2CrossRefGoogle Scholar
Aslan, S. and Erkin, T. A multi-threaded back-and-forth algorithm for planning unmanned aerial vehicles, Aeronaut. J., 2025, pp 1–26.10.1017/aer.2025.10037CrossRefGoogle Scholar
Lee, T., Leok, M. and McClamroch, N.H. Nonlinear robust tracking control of a quadrotor UAV on SE(3), Asian J. Control, 2013, 15, (2), pp 391–408.10.1002/asjc.567CrossRefGoogle Scholar
Muslimov, T.Z. and Munasypov, R.A. Coordinated UAV standoff tracking of moving target based on Lyapunov vector fields, In Proc. Int. Conf. Nonlinearity, Inf. Robot. (NIR), pp 1–5, 2020.10.1109/NIR50484.2020.9290189CrossRefGoogle Scholar