This paper discusses the development of a flight dynamics model (or digital twin) of a compact and re-configurable coaxial-propeller-based micro air vehicle (MAV) in hovering, edgewise, and maneuvering flight using a hybrid physics-based plus data-driven approach. The MAV has a mass of 366 g (0.81 lb), and features a 52 mm (2.05 in) diameter cylindrical fuselage, foldable propellers, and a two-axis gimbal thrust-vectoring mechanism for pitch and roll control. The aircraft has been successfully launched from a pneumatic cannon and has achieved stable and controlled flight. A physics-based flight dynamics model of this novel MAV has been developed using the Rotorcraft Comprehensive Analysis System (RCAS). RCAS is able to predict the translational dynamics near hover reasonably well; however, the accuracy decreases for rotational dynamics in edgewise flight, resulting in significant differences between predicted dynamics and flight-test data, known as residual dynamics. The current hybrid model utilizes the residual dynamics via a data-driven approach to correct the physics-based model. Using the measured vehicle states and control inputs, a deep neural network was trained to learn the residual forces and torques. The resulting hybrid model reduced prediction errors by 55% on average compared to the RCAS model based on pure physics.
Nyancho et al. (Thu,) studied this question.