基于深度强化学习的无人机姿态控制器设计.

In order to investigate the ability of the attitude controller of a quadcopter unmanned aerial vehicle(UAV) to possess strong target value tracking and resistance to external disturbances, a design for a quadcopter UAV attitude controller based on a reference model using deep deterministic policy gradients was proposed. The proposed method employed a neural network to directly map the state of the quadcopter unmanned aerial vehicle to its output. The reinforcement learning algorithm utilized in the paper was a combination of deep deterministic policy gradient (DDPG) and deep neural networks. In the structure of the DDPG algorithm, a reference model was further incorporated to mitigate system overshoot caused by excessive control inputs, stability and robustness of the system was enhanced. Moreover, modifications were made to the composition of rewards in reinforcement learning, The steady-state error of the system was successfully eliminated. The results show that this control method exhibits strong robustness in both target value tracking and resistance to external disturbances. It is concluded the controller performs better in terms of target value tracking and interference immunity compared to conventional controllers. [ABSTRACT FROM AUTHOR]