  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.
