Flying animals resort to fast, large-degree-of-freedom motion of flapping wings (i.e., their aerodynamic surfaces), a key feature that distinguishes them from rotary or fixed-winged robotic fliers with relatively limited motion of aerodynamic surfaces. However, it is well known that flapping-wing aerodynamics are characterised by highly unsteady and three-dimensional flows difficult to model or control. Accurate aerodynamic force predictions often rely on high-fidelity and expensive computational or experimental methods. Here, we developed a computationally efficient model that can accurately predict aerodynamic forces generated by 548 different flapping-wing motions, surpassing the predictive accuracy and generality of the existing quasi-steady models. Specifically, we trained a state-space model that dynamically mapped wing motion kinematics to aerodynamic forces and moments measured from a dynamically scaled robotic wing. This predictive model used as few as 12 states to successfully capture the unsteady and nonlinear fluid effects pertinent to force generation without explicit information of fluid flows. Also, we provided a comprehensive assessment of the control authority of key wing kinematic variables and their linear predictability of aerodynamic forces. We 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 and the most instant effects on the aerodynamic force/moment generation. Our results show that flapping flight offers inherently high force control authority and predictability, which are key to the development of agile and stable aerial fliers.
翻译:飞翔动物采用快速、大度自由运动的扇翼(即其空气动力表面),这是它们与旋转或固定翼机器人飞行机流不同的关键特征,其空气动力表面的运动相对有限。然而,众所周知,拍动动动动的空气动力的特征是极不稳定和三维的流动,难以建模或控制。准确的空气动力预测往往依赖于高纤维和昂贵的计算或实验方法。在这里,我们开发了一个计算高效模型,能够准确预测由548种不同的滚动运动产生的空气动力,超过了现有准稳定模型的预测准确性和一般性。具体地说,我们训练了一个州空间模型,以动态的方式将机翼运动动力与空气动力的动态动力和三维流动体流进行模拟或控制。这个预测性模型用于成功捕捉不固定和不线状的计算或实验方法。在这里,我们开发了一个计算高效的模型,可以准确预测由548种不同的旋转翼运动运动运动运动运动运动运动运动产生的空气动力动力,我们提供了一种最稳定的空中运动运动运动/运动运动运动运动的机翼结构,我们找到了一个稳定的机极的机极的机极的机极的机极的机极的机能控制。