Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind Disturbances - ZHANG Weizhen, HE Zhen, TANG Zhangfan (Shanghai Jiaotong Daxue xuebao)
This paper addresses the issue of perching maneuver of unmanned aerial vehicles in wind-disturbed environments, by combining the control-oriented sparse identification of nonlinear dynamics with control (SINDYc) method and the imitation deep reinforcement learning (IDRL) control strategy. The study focuses on the design of control strategies for perching maneuvers. First, a training environment for the perching system is established using domain randomization, which incorporates various wind conditions. Then, the SINDYc method is employed to learn sparse models of the perching system offline under different wind conditions, using historical data and a candidate function library, to effectively identify the wind information. Afterwards, the perching control strategy is trained using an IDRL algorithm within the training environment that encompasses multiple wind conditions, resulting in a control strategy for perching in wind-disturbed scenarios. Finally, numerical simulations are conducted to verify the effectiveness of the proposed perching control strategy in wind-disturbed environments.