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.
翻译:飞行动物使用快速、大度自由运动的扇翼,这是它们与旋转或固定翼机器人飞行机流之间区别的关键特征,它与空气动力表面运动有限的旋转或固定翼机器人飞行相区别,然而,拍动翼空气动力的特征是高度不稳定和三维流动,难以建模或控制,准确的空气动力力预测往往依赖昂贵的计算或实验方法。在这里,我们开发了一个计算高效和数据驱动的州空间模型,以动态绘制翅膀动力动力/运动与空气动力/运动的动态图谱。这一模型经过总共548种不同的拍动运动的训练和测试,超过了现有准稳定模型的准确性和一般性。这一模型使用了12个州来捕捉与在没有明确流体流动信息的情况下产生动力相关的不稳定和非线性液体效应。我们还对关键翼动力变量的控制权威进行了全面评估,发现瞬时空空气动力/运动力量/运动在半三角周期内基本上可以预测。此外,攻击的角度、最强的空中运动运动运动运动运动运动运动运动运动运动运动运动运动,以及最稳定的空中运动和运动运动结果可以形成稳定的飞行稳定、最稳定的飞行控制。