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 atti-tude modal changes of UAVs during landing exhibit repetitive patterns, low-altitude wind disturbances and random initial state devia-tions 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 conver-gence. 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