Dynamic Modelling and State Estimation of a High Speed Racing Drone

Abstract

Autonomous drone racing has taken a turn for the better in recent years. Drones are becoming faster and implementing better state-of-the-art control techniques to overcome different challenges. With advancements in the fields of computer vision, machine learning, and artificial intelligence, the final goal of autonomous drones is to be quicker than human-piloted racing drones. Increasing the speed of autonomous drones increases the risks associated with flying them. Time-optimal control algorithms have been identified as a method of implementing
aggressive maneuvers to fly drones at high speeds throughout the course of the race. These methods require precise state-estimates. This research work identifies a model for the rate controller. The work also includes an implementation of a state estimation model with drag compensation, also merging a pre-existing refined thrust model with Coriolis effects. With the idea of developing a state estimation model for a racing drone, the model is improved to
include flight envelopes involving motor saturations.