Adaptive Stabilization Control by Deep Reinforcement Learning for Hovering Drone Surveillance

This paper proposes an adaptive stabilization control mechanism by using deep reinforcement learning (DRL) for hovering drones that have to execute a surveillance task for a long time. For long-endurance flights, we design and implement a buoyancy-aided autonomous aerial vehicle (AAV) that can use buoyancy lift to decrease the weight and increase the battery capacity so that the flight time can be significantly extended. However, the balloons of the buoyancy-aided AAV can cause “an inverted pendulum effect” and an instability issue on the drone attitude because the increased surface is easily affected by the gusty wind. We propose a buoyancy-aided adaptive stabilization control (BAASC) method with the DRL to stabilize the attitude and extend the flight time of the quadrotor-based buoyancy-aided AAV. This proposed model can immediately control the speeds of all rotors to balance the attitude based on the current state of the drone. Therefore, the degree of swing can be stabilized, and the inverted pendulum effect can be eliminated. The experimental results reveal that the designed buoyancy-aided AAV with the proposed BAASC scheme can effectively stabilize the attitude to extend the flight time by 112.8% compared with a nonbuoyancy-aided AAV under a gusty wind disturbance.