  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.
