This paper presents a comprehensive survey on anti-disturbance control methods for quadrotor unmanned aerial vehicles landing on moving platforms. First, aerodynamic disturbances are analyzed for quadrotor unmanned aerial vehicles in the landing scenario. It is indicated that aerodynamic disturbances will have stronger intensity and faster dynamics with the rise of platform speeds. These complex disturbances will seriously deteriorate the landing accuracy and even cause quadrotor unmanned aerial vehicles to crash. Second, the hard landing scheme and soft landing scheme as well as their performances are discussed and summarized. The hard landing scheme aims to eliminate aerodynamic disturbances by stopping rotors, while the soft landing scheme aims to suppress the effects of aerodynamic disturbances by using feedback control and feedforward compensation. Comparatively, the soft landing scheme with disturbance feedforward can realize reliable and precise landing on moving platforms. However, further improvements are still required in disturbance estimation accuracy for quadrotor unmanned aerial vehicles landing on fast platforms. Finally, the future directions are discussed in this area.
AbdelmaksoudSIMailahMAbdallahAM (2020) Control strategies and novel techniques for autonomous propeller craft unmanned aerial vehicles: a review. IEEE Access8: 195142–195169.
2.
AliZALiX (2020) Controlling of an under-actuated quadrotor UAV equipped with a manipulator. IEEE Access8: 34664–34674.
3.
AllibertGAbeywardenaDBanguraM, et al. (2014) Estimating body-fixed frame velocity and attitude from inertial measurements for a quadrotor vehicle. In: Proceedings of the IEEE Conference on Control Applications. IEEE, 978–983.
4.
BaiLYangYGuoC, et al. (2019) Camera assisted received signal strength ratio algorithm for indoor visible light positioning. IEEE Communications Letters23(11): 2022–2025.
5.
BorowczykD-TNguyenAP-VNguyenDQ, et al. (2017) Autonomous landing of a quadcopter on a high-speed ground vehicle. Journal of Guidance, Control, and Dynamics40(9): 2373–2380.
6.
BouaissRMechgougATaleb-AhmedA (2023) Visual soft landing of an autonomous quadrotor on a moving pad using a combined fuzzy velocity control with model predictive control. Signal, Image and Video Processing17(1): 21–30.
CaiJGunasekaranSOlM (2023) Effect of partial ground and partial ceiling on propeller performance. Journal of Aircraft60(3): 648–661.
9.
CaoQLiuZYuH, et al. (2024) Autonomous landing of the quadrotor on the mobile platform via meta reinforcement learning. IEEE Transactions on Automation Science and Engineering22: 2269–2280.
10.
CengizSKUcunL (2022) Optimal controller design for autonomous quadrotor landing on moving platform. Simulation Modelling Practice and Theory119: 102565.
11.
ChakravarthyAGrantDTLindR (2012) Time-varying dynamics of a micro air vehicle with variable-sweep morphing. Journal of Guidance, Control, and Dynamics35(3): 890–901.
12.
ChenW-H (2004) Disturbance observer based control for nonlinear systems. IEEE/ASME Transactions on Mechatronics9(4): 706–710.
13.
ChenW-HYangJGuoL, et al. (2016) Disturbance-observer-based control and related methods—an overview. IEEE Transactions on Industrial Electronics63(2): 1083–1095.
14.
ChenMXiongSWuQ (2021) Tracking flight control of quadrotor based on disturbance observer. IEEE Transactions on Systems, Man, and Cybernetics: Systems51(3): 1414–1423.
15.
DingWHuangH (2023) Research on dynamic landing guidance algorithm for the maritime quadrotor. Ocean Engineering271: 113775.
16.
GaoYJiJWangQ, et al. (2023) Adaptive tracking and perching for quadrotor in dynamic scenarios. IEEE Transactions on Robotics40: 499–519.
17.
GeronelRSBuenoDD (2024) Adaptive sliding mode control for vibration reduction on UAV carrying a payload. Journal of Vibration and Control31: 108336.
18.
GhasemiFParivashFEbrahimianS (2022) Autonomous landing of a quadrotor on a moving platform using vision-based FOFPID control. Robotica40(5): 1431–1449.
19.
GhommamJSaadM (2017) Autonomous landing of a quadrotor on a moving platform. IEEE Transactions on Aerospace and Electronic Systems53(3): 1504–1519.
20.
HafezTMarascoAJGivigiSN, et al. (2015) Solving multi-UAV dynamic encirclement via model predictive control. IEEE Transactions on Control Systems Technology23(6): 2251–2265.
21.
HanQLiuZSuH, et al. (2023) Filter-based disturbance observer and adaptive control for Euler-Lagrange systems with application to a quadrotor UAV. IEEE Transactions on Industrial Electronics70(8): 8437–8445.
22.
HassanMASuCLPouJ, et al. (2022) DC shipboard microgrids with constant power loads: a review of advanced nonlinear control strategies and stabilization techniques. IEEE Transactions on Smart Grid13(5): 3422–3438.
23.
HassaniHMansouriAAhaitoufA (2024) Design and real-time implementation of a robust backstepping non-singular integral terminal sliding mode attitude control for a quadrotor aircraft. Journal of Vibration and Control31(7–8): 1278–1293.
24.
HeWYuanL (2023) Fixed-time controller of visual quadrotor for tracking a moving target. Journal of Vibration and Control30(17–18): 3746–3765.
25.
HospedalesTAntoniouAMicaelliP, et al. (2022) Meta-learning in neural networks: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence44(9): 5149–5169.
26.
HuangDLiHLiX (2020) Formation of generic UAVs-USVs system under distributed model predictive control scheme. IEEE Transactions on Circuits and Systems II: Express Briefs67(12): 3123–3127.
27.
IbarraERojo-RodriguezEGJimenez-izarragaMA, et al. (2021) Hopf bifurcation for UAV path planning in autonomous surveillance and landing on an UGV. Journal of Intelligent and Robotic Systems102: 41.
28.
JeonHSongJLeeH, et al. (2021) Modeling quadrotor dynamics in a wind field. IEEE/ASME Transactions on Mechatronics26(3): 1401–1411.
29.
KimJWJungYDLeeDS, et al. (2016) Landing control on a mobile platform for multi-copters using an omnidirectional image sensor. Journal of Intelligent & Robotic Systems84(1–4): 529–541.
30.
LeiWCLiCYChenMZQ (2019) Robust adaptive tracking control for quadrotors by combining PI and self-tuning regulator. IEEE Transactions on Control Systems Technology27(6): 2663–2671.
31.
LianMengWLinZ, et al. (2022) Adaptive attitude control of a quadrotor using fast nonsingular terminal sliding mode. IEEE Transactions on Industrial Electronics69(2): 1597–1607.
32.
LimJLeeTPyoS, et al. (2022) Hemispherical infrared (IR) marker for reliable detection for autonomous landing on a moving ground vehicle from various altitude angles. IEEE/ASME Transactions on Mechatronics27: 485–492.
33.
LinJWangYMiaoZ, et al. (2022) Low-complexity control for vision-based landing of quadrotor UAV on unknown moving platform. IEEE Transactions on Industrial Informatics18(8): 5348–5358.
34.
LinJWangYMiaoZ, et al. (2023) Robust image-based landing control of a quadrotor on an unpredictable moving vehicle using circle features. IEEE Transactions on Automation Science and Engineering20(2): 1429–1440.
35.
LinJMiaoZWangY, et al. (2024) Vision-based safety-critical landing control of quadrotors with external uncertainties and collision avoidance. IEEE Transactions on Control Systems Technology32: 1310–1322.
36.
LiuZWangCChenK, et al. (2022) Vision-based autonomous landing on unprepared field with rugged surface. IEEE Sensors Journal22(18): 17914–17923.
37.
LiuSWangZShengX, et al. (2024) Hitchhiker: a quadrotor aggressively perching on a moving inclined surface using compliant suction cup gripper. IEEE Transactions on Automation Science and Engineering21(3): 2552–2563.
38.
LunguMChenMDinuD-A (2022) Backstepping- and sliding mode-based automatic carrier landing system with deck motion estimation and compensation. Aerospace9: 644.
39.
LunguMDinuD-AChenM, et al. (2023) Inverse optimal control for autonomous carrier landing with disturbances. Aerospace Science and Technology139: 108382.
40.
MaMWangTQiuJ, et al. (2021) Adaptive fuzzy decentralized tracking control for large-scale interconnected nonlinear networked control systems. IEEE Transactions on Fuzzy Systems29(10): 3186–3191.
41.
NguyenNPOhHMoonJ (2022) Continuous nonsingular terminal sliding mode control with integral-type sliding surface for disturbed systems: application to attitude control for quadrotor UAVs under external disturbances. IEEE Transactions on Aerospace and Electronic Systems58(6): 5635–5660.
42.
RubioJDJPerez-CruzJHZamudioZ, et al. (2014) Comparison of two quadrotor dynamic models. IEEE Latin American Transactions12(4): 531–537.
43.
SaifA-WAAliyuADhaifallahMA, et al. (2018) Decentralized backstepping control of a quadrotor with tilted-rotor under wind gusts. International Journal of Control, Automation, and Systems16(5): 2458–2472.
44.
Sanchez-CuevasPHerediaGOlleroA (2017) Characterization of the aerodynamic ground effect and its influence in multirotor control. International Journal of Aerospace Engineering2017: 1823056.
45.
SerraPCunhaRHamelT, et al. (2016) Landing of a quadrotor on a moving target using dynamic image-based visual servo control. IEEE Transactions on Robotics32(6): 1524–1535.
46.
ShaoLGuoZYangJ, et al. (2024) Vision-based quadrotor rapid landing control with an uncooperative platform: an alternating predictive observer approach. IEEE Transactions on Intelligent Vehicles: 1–13. Epub ahead of print 27 March 2024. DOI: 10.1109/TIV.2024.3382207.
47.
ShenJWangBChenBM, et al. (2023) Review on wind resistance for quadrotor UAVs: modeling and controller design. Unmanned Systems11(1): 5–15.
48.
ShiDWuZChouW (2020) Anti-disturbance trajectory tracking of quadrotor vehicles via generalized extended state observer. Journal of Vibration and Control26(13–14): 1173–1186.
49.
SunLHuangYZhengZ, et al. (2020) Adaptive nonlinear relative motion control of quadrotors in autonomous shipboard landings. Journal of the Franklin Institute357(18): 13569–13592.
50.
VafamandNArefiMM (2022) Robust neural network-based backstepping landing control of quadrotor on moving platform with stochastic noise. International Journal of Robust and Nonlinear Control32(4): 2007–2026.
51.
WeiQYangZSuH, et al. (2024) Online adaptive dynamic programming for optimal self-learning control of VTOL aircraft systems with disturbances. IEEE Transactions on Automation Science and Engineering21(1): 343–352.
52.
XiaKLeeSSonH (2020) Adaptive control for multi-rotor UAVs autonomous ship landing with mission planning. Aerospace Science and Technology96: 105549.
53.
XiaKShinMChungW, et al. (2022) Landing a quadrotor UAV on a moving platform with sway motion using robust control. Control Engineering Practice128: 105288.
54.
XiaKLeeS-MChungW, et al. (2023) Moving target landing of a quadrotor using robust optimal guaranteed cost control. IEEE/CAA Journal of Automatica Sinica10(3): 819–821.
55.
XiaKHuangYZouY, et al. (2024) Reinforcement learning control for moving target landing of VTOL UAVs with motion constraints. IEEE Transactions on Industrial Electronics71(7): 7735–7744.
56.
XuSWuZ (2024) Adaptive learning control of robot manipulators via incremental hybrid neural network. Neurocomputing568: 127045.
57.
XuXWangZDengY (2019) A software platform for vision-based UAV autonomous landing guidance based on markers estimation. Science China Technological Sciences62: 1825–1836.
58.
Xuan-MungNNguyenNPNguyenT, et al. (2022) Quadcopter precision landing on moving targets via disturbance observer-based controller and autonomous landing planner. IEEE Access10: 83580–83590.
59.
YanRWuZ (2017) Attitude stabilization of flexible spacecrafts via extended disturbance observer based controller. Acta Astronautica133: 73–80.
60.
YangJChenWLiS, et al. (2017) Disturbance/uncertainty estimation and attenuation techniques in PMSM drives-a survey. IEEE Transactions on Industrial Electronics64(4): 3273–3285.
61.
YangHChengLZhangJ, et al. (2021) Leader–follower trajectory control for quadrotors via tracking differentiators and disturbance observers. IEEE Transactions on Systems, Man, and Cybernetics: Systems51(1): 601–609.
62.
YangLWangXLiuZ, et al. (2024) Robust online predictive visual servoing for autonomous landing of a rotor UAV. IEEE Transactions on Intelligent Vehicles: 1–17. Epub ahead of print 4 April 2024. DOI: 10.1109/TIV.2024.3385283.
63.
YouSSonYSGuiY, et al. (2023) Gradient-descent-based learning gain for backstepping controller and disturbance observer of nonlinear systems. IEEE Access11: 2743–2753.
64.
YuXYangJLiS (2020) Disturbance observer-based autonomous landing control of unmanned helicopters on moving shipboard. Nonlinear Dynamics102(1): 131–150.
65.
YuLHeGWangX, et al. (2021) A novel fixed-time sliding mode control of quadrotor with experiments and comparisons. IEEE Control Systems Letters6: 770–775.
66.
YuanYDuanH (2024) Adaptive learning control for a quadrotor unmanned aerial vehicle landing on a moving ship. IEEE Transactions on Industrial Informatics20(1): 534–545.
67.
YuanYDuanHZengZ (2024) Prescribed performance evolution control for quadrotor autonomous shipboard landing. IEEE/CAA Journal of Automatica Sinica11(5): 1151–1162.
68.
ZengQJinYYuH, et al. (2023) A UAV localization system based on double UWB tags and IMU for landing platform. IEEE Sensors Journal23(9): 10100–10108.
69.
ZhangXWangYZhuG, et al. (2021) Compound adaptive fuzzy quantized control for quadrotor and its experimental verification. IEEE Transactions on Cybernetics51(3): 1121–1133.
70.
ZhangYWuZWeiT (2024) Precise landing on moving platform for quadrotor UAV via extended disturbance observer. IEEE Transactions on Intelligent Vehicles: 1–10. Epub ahead of print 19 March 2024. DOI: 10.1109/TIV.2024.3376954.
71.
ZhaoWLiuHWangX (2021) Robust visual servoing control for quadrotors landing on a moving target. Journal of the Franklin Institute358(4): 2301–2319.
72.
ZhouDZhengZGuanZ, et al. (2023) Barrier Lyapunov function based fixed-time control for automatic carrier landing with disturbances. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering237(1): 177–190.
