发表机构
Eindhoven University of Technology（埃因霍温理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种利用低成本皮托管传感器测量无人机前方风况，并将其融入非线性模型预测控制（MPC）的机载风前视控制方法，以提升旋翼无人机在阵风条件下的悬停性能，实验表明沿风向误差减少54%。
AI 中文摘要
有效的阵风抑制和稳定悬停对于自主无人机在户外运行至关重要。然而，现有的阵风抑制方法主要是反应式的，即从产生的运动或机体上的测量中推断扰动。无论哪种方式，风在得到补偿之前已经开始产生影响。在这项工作中，我们通过安装在吊杆上的低成本、低重量皮托管传感器来测量无人机前方的风，从而提前预测阵风。由此产生的风前视信息被纳入非线性模型预测控制器（MPC），该控制器在预测风扰动的同时优化无人机运动。较长的吊杆提供更长的前视时间，但会增加惯性并降低飞行性能。我们在仿真中表征了这一权衡，并表明最佳前视距离不是平台的固定属性，而是随风速以及无人机响应速度的变化而变化。室内硬件实验证实了这一趋势，并表明所提出的控制器相对于PX4基线和另一种对风不敏感的MPC，显著改善了悬停性能。户外实验表明，沿风向的误差相对于基线减少了54%，证明单个风向对齐的传感器可以显著提高悬停性能。
英文摘要
Effective wind gust rejection and stable hovering are critical for the outdoor operation of autonomous drones. However, existing gust rejection methods are primarily reactive, inferring the disturbance from the resulting motion or measuring it at the airframe. Either way, the wind has already begun to act before it can be compensated. In this work, we anticipate the gust instead by measuring the wind ahead of the drone with a low-cost, low-weight pitot-static sensor mounted on a boom. The resulting wind preview is incorporated into a nonlinear model predictive controller (MPC), which optimizes the drone motion while anticipating wind disturbances. A longer boom offers more preview time but adds inertia and degrades flight performance. We characterize this trade-off in simulation and show that the optimal preview distance is not a fixed property of the platform, but shifts with the wind speed and with how quickly the drone can respond. Indoor hardware experiments confirm the trend and show that the proposed controller substantially improves hover performance against a PX4 baseline and an otherwise identical wind-unaware MPC. Outdoor experiments show that the error along the wind direction is reduced by 54 percent with respect to the baseline, demonstrating that a single wind-aligned sensor can significantly improve hovering performance.
CommentsSubmitted to ICRA 2027
