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 characterized 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.

飞行动物依靠快速、大自由度的扑翼运动，这是它们区别于气动表面运动有限的旋转或固定翼机器人飞行器的一个关键特征。然而，扑翼空气动力学的特点是高度不稳定和三维流动难以建模或控制，准确的气动力预测通常依赖于昂贵的计算或实验方法。在这里，我们开发了一种计算高效且数据驱动的状态空间模型，可以动态地将机翼运动学映射到空气动力/力矩。该模型总共使用了 548 种不同的扑翼运动进行了训练和测试，超越了现有准稳态模型的准确性和通用性。该模型使用 12 种状态来捕获与力生成相关的不稳定和非线性流体效应，而无需明确的流体流动信息。我们还对关键机翼运动学变量的控制权限进行了全面评估，发现瞬时气动力/力矩在很大程度上可以通过半冲程周期内的机翼运动历史来预测。此外，迎角、法向加速度和俯仰运动对空气动力/力矩产生的影响最强。我们的结果表明，扑动飞行本质上提供了较高的力控制权威和可预测性，这对于开发敏捷和稳定的空中飞行员来说是关键。