让无人机精准降落在移动平台上，还能自动适应传感器波动。
Fixed-Time Dynamic Landing of Quadrotors using Adaptive Unscented Kalman Filtering and Nonlinear Model Predictive Control
- 用自适应无迹卡尔曼滤波实时调整噪声参数，提升定位鲁棒性。
- 结合非线性模型预测控制与最小加速度轨迹规划，实现精确触地时间控制。
- 适合需要高精度动态着陆的无人机应用，如舰载降落或无人配送。
本文提出一种多旋翼无人机在运动平台上的动态着陆估计与控制框架。该方法将非线性模型预测控制与实时最小加速度轨迹规划相结合，强制执行预定触地时间，确保终端下降阶段的一致性。为应对时变感知质量带来的影响，采用在线更新过程和测量噪声统计的自适应无迹卡尔曼滤波器以增强鲁棒性。此外，通过参考可行性分析表明，在标准跟踪假设下，最小加速度参考可产生有界的推力与扭矩指令。所提框架在仿真与硬件实验中均得到验证，结果表明其具有可重复的着陆性能，并相较于基于EKF/UKF的方法提升了平台速度预测精度。
原文摘要 · Abstract (English)
This paper introduces an estimation and control framework for dynamic landing of multi-rotor uncrewed aerial vehicles on moving platforms. The proposed method integrates nonlinear model predictive control with a real-time minimum-jerk trajectory planner that enforces a prescribed touchdown time, enabling consistent timing during the terminal descent. To enhance robustness in the presence of time-varying sensing quality, we utilize an adaptive unscented kalman filter that updates the process and measurement noise statistics online. In addition, we provide a reference feasibility analysis showing that minimum-jerk references induce bounded thrust and torque commands under standard tracking hypotheses. The proposed framework is evaluated in simulation and hardware experiments, and it is shown to achieve repeatable landings and improved platform velocity prediction accuracy relative to EKF/UKF-based methods.
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