Event-based neural learning for quadrotor control

The design of a simple and adaptive flight controller is a real challenge in aerial robotics. A simple flight controller often generates a poor flight tracking performance. Furthermore, adaptive algorithms might be costly in time and resources or deep learning based methods may cause instability problems, for instance in presence of disturbances. In this paper, we propose an event-based neural learning control strategy that combines the use of a standard cascaded flight controller enhanced by a deep neural network that learns the disturbances in order to improve the tracking performance. The strategy relies on two events: one allowing the improvement of tracking errors and the second to ensure closed-loop system stability. After a validation of the proposed strategy in a ROS/Gazebo simulation environment, its effectiveness is confirmed in real experiments in the presence of wind disturbance.