TL;DR
This study introduces a data-driven state-space model that efficiently predicts aerodynamic forces in flapping flight, revealing high control authority and predictability of wing kinematics for agile aerial maneuvering.
Contribution
A novel, computationally efficient state-space model trained on extensive data that outperforms existing models in predicting unsteady aerodynamic forces in flapping flight.
Findings
Model surpasses quasi-steady models in accuracy and generality.
Aerodynamic forces are largely predictable within a half-stroke cycle.
Key wing kinematic variables like angle of attack and pitching motion strongly influence force generation.
Abstract
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…
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