Data-Driven Identification of Flapping Wing Aerodynamic Forces using Covariance Matrix Adaptation Evolution Strategy (CMA-ES)

Jonish Abisheck Joseph¹    Yash Prashant Adivarekar²    Bhamidipati Srinath Dhatre²    Harikumar Kandath²   

¹ Department of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Goa, India    ² Robotics Research Centre (RRC), IIIT Hyderabad, Hyderabad, India   

System identification for Flapping Wing Unmanned Aerial Vehicles (FWUAVs) typically relies on simplified unsteady aerodynamics into time-averaged models. We propose an aerodynamic framework that identifies unsteady force parameters, including apparent mass and aeroelastic effects, using IMU and airspeed data. Utilizing a modified DeLaurier’s formulation, our model estimates aerodynamic coefficients that approximate unsteady effects within a computationally efficient framework. Model parameters are optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The framework was trained and validated using experimental flight data from a 1.4 m wingspan prototype.The results demonstrate good predictive capability, achieving a reduction in Root Mean Square Error (RMSE) of 36% when compared to the baseline model without incorporating unsteady effects.