Reinforcement learning based recovery flight control for flapping-wing micro-aerial vehicles under extreme attitudes

This article deals with the recovery flight problem of flapping-wing micro-aerial vehicles under extreme attitude by using a reinforcement learning approach. First, the reinforcement learning-based control policy is proposed to enable the flapping-wing micro-aerial vehicles to be recovery flight rapidly and keep the angular acceleration as small as possible. Then, a hybrid control approach is designed to significantly improve the flight stability by combining the reinforcement learning-based control approach with the proportional-derivative control approach. Finally, simulation results show the effectiveness of the reinforcement learning-based method and the hybrid control method for the flapping-wing micro-aerial vehicles under extreme attitudes.