Neural Network Assisted Flight Dynamics Modeling of a Tailsitter UAS with Experimental Validation

This paper discusses the development of a quantitatively-accurate non-linear hybrid flight dynamics model of a hover-capable Air-Launched Tailsitter Unmanned Aerial System (ALUAS) in order to 1) understand its dynamics during complicated maneuvers, and 2) provide a high-fidelity framework to develop novel control laws. Wind tunnel tests were conducted on a 1:1 scale model of the full aircraft to measure the airloads, which were used in the simulation as a lookup table. Flight tests of the ALUAS were performed in hover, transition, and cruise to collect a large amount of unique state measurements by providing large excitations to induce highly transient motion. The flight dynamics predictions using Rotorcraft Comprehensive Analysis System (RCAS) software were then compared with experimental flight test data. To correct any discrepancies in the RCAS physics-based predictions, a correction was learned from the experimental measurements, making use of the large amount of collected flight test data. Using a neural network to learn this correction, the end result was a quantitatively accurate neural network assisted flight dynamics model. The accuracy of current simulations in complex flight states successfully demonstrates the applicability of the proposed methodology for correcting the dynamics model of novel out-of-the-box aircraft configurations.