Learning trajectory tracking under disturbances using a differentiable simulator for drone control
Abstract —Drones are being used more frequently in a variety of industries, including transportation, inspection, and videogra- phy. However, the limited capacity of onboard computing power, constrained by weight and energy consumption, presents a signifi- cant challenge. Therefore, there is a need to enhance the efficiency and accuracy of control systems. This project investigates the potential of learning-based methods for trajectory tracking in the presence of disturbances, such as wind. By using a differentiable simulator for drone control, we have shown that modifying a pretrained model can improve noise rejection capabilities. Furthermore, we introduce an effective wind estimation method. Our findings suggest a substantial enhancement in drone control under adverse conditions, which could lead to more robust and reliable aerial robotics applications. I NTRODUCTION With the increasing capabilities of hardware a...