A Data-Driven Approach to Onboard Aerodynamic Sensing and Estimation for Tailsitter UAVs

Tailsitter configurations that operate in both fixed and rotary wing flight modes are typically capable of generating large control forces and moments, making them inherently capable of rapid transitions and aggressive maneuvers. However, harnessing these capabilities requires feedback control strategies that can effectively estimate the non-linear aerodynamics loads involved to successfully exploit them. This paper describes initial steps in combining an onboard flow sensing strategy with a data-driven approach to estimating inflight air loads. A neural network is trained to use measurements from a multi-hole probe to predict the output from a set of pressure sensors embedded in a wing section undergoing a series of pitch motions in a wind tunnel. We hypothesize that this limited context of emulating a sensor network represents a focused and compartmentalized approach to applying emerging data-driven techniques to challenging aeronautical problems. We compare estimation results from a set of neural networks with varying input configurations to assess the feasibility of our approach and the significance of different sensing modalities on overall performance. Current results show that a gated recurrent network (GRU) trained with unsteady pressure measurements was able to predict the chordwise pressure distribution on a pitching NACA 2412 airfoil using probe measurements, reproducing the transient and non-linear effects observed in our dataset.