发表机构
University of California, Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种融合差分平坦性轨迹生成与非线性模型预测控制（NMPC）的统一框架，嵌入感知风的采样策略，经仿真与实飞验证，可提升固定翼无人机复杂轨迹跟踪的精度与鲁棒性。
AI 中文摘要
固定翼无人机（UAV）的规划与控制极具挑战性，原因在于其非线性动力学、空气动力学极限以及环境干扰。差分平坦性为生成快速可行轨迹提供了一种有原则的方法，但其应用大多局限于无模型控制器，这类控制器缺乏预测能力且需要调参。本文提出了一种统一框架，将基于差分平坦性的轨迹生成与非线性模型预测控制（NMPC）相结合，把计算高效的规划与具备预测能力、能感知约束的控制融为一体。为进一步提升鲁棒性，我们在NMPC框架内引入了一种感知风的采样策略，可生成动态可行的参考轨迹，该轨迹能主动考虑风干扰，同时严格遵守空气动力学和控制输入约束。我们通过大量仿真和实际飞行实验对所提框架进行了验证，结果表明，所提框架对复杂轨迹的跟踪精度和鲁棒性均有所提升，尤其是在强风条件下采用所提感知风的采样策略时，提升效果更为显著。
英文摘要
Planning and control of fixed-wing Unmanned Aerial Vehicles (UAVs) are challenging due to nonlinear dynamics, aerodynamic limits, and environmental disturbances. Differential flatness offers a principled way to generate fast, feasible trajectories, but its use has largely been confined to model-free controllers, which lack predictive capabilities and demand tuning. In this paper, we propose a unified framework that integrates differential flatness-based trajectory generation with Nonlinear Model Predictive Control (NMPC), combining computationally efficient planning with predictive, constraint-aware control. To further improve robustness, we introduce a wind-aware sampling strategy embedded within the NMPC framework, enabling the generation of dynamically feasible reference trajectories that proactively account for wind disturbances while strictly enforcing aerodynamic and control input constraints. We validate the proposed framework through extensive simulations and real-world flight experiments, demonstrating improved tracking accuracy and robustness for complex trajectories, particularly when using the proposed wind-aware sampling strategy under strong wind conditions.
