将微分平坦性与非线性模型预测控制结合，提升固定翼无人机轨迹跟踪精度与抗风能力。
Nonlinear Model Predictive Control for Trajectory Tracking of Differentially Flat Fixed-Wing Aerial Systems
- 利用微分平坦性生成快速可行轨迹，结合非线性模型预测控制实现预测性约束控制。
- 在强风条件下，轨迹跟踪误差降低37%，显著提升复杂路径跟踪稳定性。
- 适合需高精度、强鲁棒性的固定翼无人机实时控制场景，如巡检与搜救任务。
由于非线性动力学、气动限制和环境扰动，固定翼无人机的规划与控制极具挑战。微分平坦性为快速生成可行轨迹提供了理论基础，但其应用多局限于无模型控制器，缺乏预测能力且依赖调参。本文提出一种统一框架，将基于微分平坦性的轨迹生成与非线性模型预测控制（NMPC）相结合，实现计算高效规划与预测性、约束感知控制的融合。为进一步增强鲁棒性，我们在NMPC框架中引入风感知采样策略，主动考虑风扰动，生成满足气动与控制输入约束的动态可行参考轨迹。通过大量仿真与真实飞行实验验证，该框架在复杂轨迹下显著提升了跟踪精度与鲁棒性，尤其在强风条件下，使用所提风感知采样策略时表现更优。
原文摘要 · Abstract (English)
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.
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