Mixture of Experts for Unmanned Aerial Vehicle Motor Thrust Models

Abstract

This study aims to analyze three unmanned aerial vehicles’ static and dynamic motor thrust models and to compare them with flight test telemetry motor angular velocity data as ground truth. This comparison determines which model is the most accurate based on the root mean square of the difference between the motor models and the telemetry data values. The Burgers and Staples models are dynamic thrust models with nonlinear drag, while the Gibiansky model is a static thrust model with linear drag. The mixture of experts (MoE) architecture assigns weights to each thrust model through a gating network such that higher weights map to the more accurate models. Flight tests include two maneuvers: triangle and shoelace loop. The triangle maneuver uses the cornering option to stop and turn at each waypoint. In contrast, the shoelace loop maneuver uses the curved option to fly smooth curves at waypoints instead of stopping and turning at each waypoint. Simulation results of the motor thrust models use the telemetry data to compute the motor angular velocities with tuned model parameters tailored to the type of maneuver. Regarding motor angular velocity estimation, the Gibiansky and Burgers models tend to overestimate, while the Staples model tends to underestimate. The combination of the simulation results of the models with the mixture of experts allows us to achieve higher accuracy than any of the individual models.