Reinforcement Learning for Attitude Control of a Flapping Drone with left-and-right wings independent drive system

Abstract

We have developed an unmanned aerial vehicle (UAV) equipped with independently driven flapping wings. It can change its flight attitude seamlessly between hovering and level flight. The main method employed for controlling its flight attitude is PID control with an assistance of learning P gain parameter using Deep Q Network (DQN). The program is specifically designed for controlling the yaw amplitude of the airframe. The result shows that the PID-DQN hybrid method is much more effective to the ongoing attitude change than the simple PID control without DQN.