历史轨迹驱动无人机自主着陆迭代学习控制

doi:10.7527/S1000-6893.2025.32634

Abstract

Abstract:

Small fixed-wing Unmanned Aerial Vehicles （UAVs） have been widely adopted in low-altitude economic sectors such as forestry and firefighting， where reliable autonomous landing capabilities are essential for mission execution. Although the positional and attitude modal changes of UAVs during landing exhibit repetitive patterns， low-altitude wind disturbances and random initial state deviations frequently cause fluctuations in trajectory length， compromising landing safety. To address this issue， this paper proposes a historical-trajectory-driven Iterative Learning Control （ILC） scheme. At the iterative dimension， the scheme optimizes the desired landing trajectory by leveraging historical trajectory data， indirectly reducing tracking errors while maintaining compatibility with existing flight control frameworks and avoiding frequent parameter adjustments. At the temporal dimension， the Dynamic Time Warping （DTW） algorithm is employed to nonlinearly align multi-sortie historical trajectories across time and space. This approach overcomes the traditional iterative learning constraint requiring strictly identical desired trajectories while ensuring iterative convergence. Simulation results demonstrate that， compared to conventional autopilots， this scheme reduces lateral and longitudinal tracking errors in landing trajectories by over 50%， significantly enhancing the success rate and safety of autonomous landings.

Key words: small fixed-wing, history-trajectory-driven, iterative learning control, dynamic time warping, reference trajectories optimization

CLC Number:

Qiuyang TIAN, Zelin WANG, Tianjiang HU. History-trajectory-driven iterative learning control for autonomous fixed-wing UAV landing[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(7): 332634.

Figures/Tables 8

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References 26

[1]	张军，陈磊，高智杰，等. 低空无人机技术研究现状与展望［J］. 中国工程科学， 2025， 27（2）： 73-85.
	ZHANG J， CHEN L， GAO Z J， et al. Low-altitude unmanned aerial vehicle technology： Current status and prospects［J］. Strategic Study of CAE， 2025， 27（2）： 73-85 （in Chinese）.
[2]	蒲钒，陈志杰，刘杨，等. 数字低空融合运行空中交通管理技术［J］. 航空学报， 2025， 46（11）： 531331.
	PU F， CHEN Z J， LIU Y， et al. Air traffic management technologies for digital low-altitude integrated operations［J］. Acta Aeronautica et Astronautica Sinica， 2025， 46（11）： 531331 （in Chinese）.
[3]	ZHAO R， LI Y， GAO F， et al. Multi-agent constrained policy optimization for conflict-free management of connected autonomous vehicles at unsignalized intersections［J］. IEEE Transactions on Intelligent Transportation Systems， 2024， 25（6）： 5374-5388.
[4]	QI P Y， ZHAO X W， PALACIOS R. Autonomous landing control of highly flexible aircraft based on lidar preview in the presence of wind turbulence［J］. IEEE Transactions on Aerospace and Electronic Systems， 2019， 55（5）： 2543-2555.
[5]	LEE S， LEE J， LEE S， et al. Sliding mode guidance and control for UAV carrier landing［J］. IEEE Transactions on Aerospace and Electronic Systems， 2019， 55（2）： 951-966.
[6]	CHEN C， TAN W Q， QU X J， et al. A fuzzy human pilot model of longitudinal control for a carrier landing task［J］. IEEE Transactions on Aerospace and Electronic Systems， 2018， 54（1）： 453-466.
[7]	SADAT-HOSEINI H， FAZELZADEH S A， RASTI A， et al. Final approach and flare control of a flexible aircraft in crosswind landings［J］. Journal of Guidance， Control， and Dynamics， 2013， 36（4）： 946-957.
[8]	马国庆，田秋扬，朱波，等. 孪生场景驱动的固定翼无人机进场自主着陆控制［J］. 控制理论与应用， 2024， 41（9）： 1664-1675.
	MA G Q， TIAN Q Y， ZHU B， et al. Twin-scenario driven autolanding control for fixed-wing UAVs［J］. Control Theory & Applications， 2024， 41（9）： 1664-1675 （in Chinese）.
[9]	FIGUEIRA J C， BAZZOCCHI S， WARWICK S， et al. Nonlinear aero-propulsive modeling for fixed-wing eVTOL UAV from flight test data［J］. Journal of Aircraft， 2024， 62（2）： 300-312.
[10]	CAO R， LU K L， LIU Y B. An interpretable data-driven learning approach for nonlinear aircraft systems with noisy interference［J］. IEEE Transactions on Aerospace and Electronic Systems， 2025， 61（1）： 182-194.
[11]	王宏伦，裴云峰，倪少波，等. 飞行器无动力应急着陆域和着陆轨迹设计［J］. 航空学报， 2014， 35（5）： 1404-1415.
	WANG H L， PEI Y F， NI S B， et al. Design of emergency landing region and landing trajectory for unpowered aircraft［J］. Acta Aeronautica et Astronautica Sinica， 2014， 35（5）： 1404-1415 （in Chinese）.
[12]	PAN Z L， PI Y J. Design of an automatic landing strategy for fixed-wing aircraft based on longitudinal and lateral collaborative hierarchical control［J］. Aerospace Science and Technology， 2025， 163： 110217.
[13]	IIGUNI Y， AKIYOSHI H， ADACHI N. An intelligent landing system based on a human skill model［J］. IEEE Transactions on Aerospace and Electronic Systems， 1998， 34（3）： 877-882.
[14]	DUAN H B， CHEN L， ZENG Z G. Automatic landing for carrier-based aircraft under the conditions of deck motion and carrier airwake disturbances［J］. IEEE Transactions on Aerospace and Electronic Systems， 2022， 58（6）： 5276-5291.
[15]	BRISTOW D A， THARAYIL M， ALLEYNE A G. A survey of iterative learning control［J］. IEEE Control Systems Magazine， 2006， 26（3）： 96-114.
[16]	LI X F， HUANG D Q， CHU B， et al. Robust iterative learning control for systems with norm-bounded uncertainties［J］. International Journal of Robust and Nonlinear Control， 2016， 26（4）： 697-718.
[17]	VAN DE WIJDEVEN J， DONKERS T， BOSGRA O. Iterative Learning Control for uncertain systems： Robust monotonic convergence analysis［J］. Automatica， 2009， 45（10）： 2383-2391.
[18]	LI X F， XU J X， HUANG D Q. An iterative learning control approach for linear systems with randomly varying trial lengths［J］. IEEE Transactions on Automatic Control， 2014， 59（7）： 1954-1960.
[19]	XIA J K， ZHANG R Q， LI Y N， et al. Iterative learning control based on dynamic time warping for a class of discrete-time nonlinear systems with varying trial lengths and terminus constraint［J］. International Journal of Robust and Nonlinear Control， 2023， 33（5）： 3146-3163.
[20]	郭晓临，刘洋，林娜，等. 基于扩展状态观测器的量化无模型自适应迭代学习控制［J］. 控制理论与应用， 2025， 42（2）： 253-262.
	GUO X L， LIU Y， LIN N， et al. Extended state observer-based quantitative model-free adaptive iterative learning control［J］. Control Theory & Applications， 2025， 42（2）： 253-262 （in Chinese）.
[21]	LV S L， MAO P D， QUAN Q. Mean-field based time-optimal spatial iterative learning within a virtual tube［J］. IEEE Control Systems Letters， 2024， 8： 2021-2026.
[22]	张凡，孟德元，惠宇. 面向超机动飞行的迭代学习控制研究综述［J］. 空天技术， 2022（6）： 12-23， 66.
	ZHANG F， MENG D Y， HUI Y. Review of iterative learning control for super-maneuver flight［J］. Aerospace Technology， 2022（6）： 12-23， 66 （in Chinese）.
[23]	BEARD R W， MCLAIN T W. Small unmanned aircraft： Theory and practice［M］. Princeton： Princeton University Press， 2012： 95-118.
[24]	BALDI S， SUN D P， XIA X， et al. ArduPilot-based adaptive autopilot： architecture and software-in-the-loop experiments［J］. IEEE Transactions on Aerospace and Electronic Systems， 2022， 58（5）： 4473-4485.
[25]	SUN M X， LI W， WU L W. Dead-beat terminal sliding-mode control： A guaranteed attractiveness approach［J］. Automatica， 2021， 129： 109609.
[26]	LV S L， GAO Y， CHE J X， et al. Autonomous drone racing： time-optimal spatial iterative learning control within a virtual tube［C］∥2023 IEEE International Conference on Robotics and Automation （ICRA）. Piscataway： IEEE Press， 2023： 3197-3203.

分类	允许设置参数
风扰	水平风向，水平风速，垂直风速，湍流均方根（RMS）
载荷	初始质量，质心偏移，电池容量，载荷类型

History-trajectory-driven iterative learning control for autonomous fixed-wing UAV landing

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