Flapping Wing Aerodynamics with PRSSM

Flying animals resort to fast, large-degree-of-freedom motion of flapping
wings, a key feature that distinguishes them from rotary or fixed-winged
robotic fliers with limited motion of aerodynamic surfaces. However,
flapping-wing aerodynamics are characterised by highly unsteady and
three-dimensional flows difficult to model or control, and accurate
aerodynamic force predictions often rely on expensive computational or
experimental methods. Here, we developed a computationally efficient and
data-driven state-space model to dynamically map wing kinematics to
aerodynamic forces/moments. This model was trained and tested with a total
of 548 different flapping-wing motions and surpassed the accuracy and
generality of the existing quasi-steady models. This model used 12 states
to capture the unsteady and nonlinear fluid effects pertinent to force
generation without explicit information of fluid flows. We also provided a
comprehensive assessment of the control authority of key wing kinematic
variables and found that instantaneous aerodynamic forces/moments were
largely predictable by the wing motion history within a half-stroke cycle.
Furthermore, the angle of attack, normal acceleration, and pitching motion
had the strongest effects on the aerodynamic force/moment generation. Our
results show that flapping flight inherently offers high force control
authority and predictability, which can be key to developing agile and
stable aerial fliers.

飞行类动物依靠快速、高自由度的扑翼（flapping wings）运动，这是其与气动面运动受限的旋翼式或固定翼仿生飞行机器人的核心区别特征。然而，扑翼空气动力学以高度非定常流动与三维流动为典型特征，这类流动难以建模与控制，而精准的气动力预测往往依赖成本高昂的计算或实验手段。本研究开发了一种计算高效的数据驱动状态空间模型（state-space model），可将扑翼运动学（wing kinematics）参数动态映射至气动力/力矩。该模型共使用548种不同的扑翼运动样本进行训练与测试，其精度与泛化性能均优于现有准稳态模型（quasi-steady models）。该模型仅使用12个状态量，即可在无需流体流动显式信息的前提下，捕捉与力生成相关的非定常与非线性流体效应。本研究还对关键扑翼运动学变量的操纵效能进行了全面评估，发现瞬时气动力/力矩在很大程度上可通过半冲程周期（half-stroke cycle）内的翼面运动历史进行预测。此外，攻角（angle of attack）、法向加速度（normal acceleration）与俯仰运动（pitching motion）对气动力/力矩生成的影响最为显著。研究结果表明，扑翼飞行本身具备优异的力控制效能与可预测性，这可为开发敏捷且稳定的空中飞行机器人提供核心支撑。