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.

This dataset includes the data used in the manuscript "State-space aerodynamic model reveals high force control authority and predictability in flapping flight".  The code for the PRSSM model and to analyze the data is provided in "https://github.com/yagiz-bayiz/flapping-wing-aerodynamics-prssm". See the manuscript for more details ("https://arxiv.org/abs/2103.07994").The aerodynamics data ("flapping_wing_aerodynamics.mat")  used to train and test the Probabilistic Recurrent State-Space Model (PRSSM) was generated by the dynamically scaled robotic wing designed by the BioRob-InFL lab. The data set includes all 548 trajectories used in the analysis. The measured positions, forces, and moments were denoised and post-processed by the robotic wing target PC to remove the sensor bias, buoyancy, and gravitational effects. Furthermore, the data was downsampled and organized in the postprocessing to have a 25Hz sampling time and 470 data points for each trajectory.