【摘要】 为了解决无人水面船（USV）在复杂海洋环境中路径跟随的控制问题，本文构建基于MAVLink的通信系统，实现领航船舶与受控船舶间的实时状态传输，确保受控船舶能够根据领航船舶的实时位置、速度等信息进行动态调整，并利用深度Q网络（Deep Q-Network,DQN）的学习方法使受控船舶能够自主学习最优的航行路径，从而提升跟随精度。在通信不稳定的条件下，采用反步控制（Backstepping Control,BC）进行状态预测并实时反馈补偿，从而确保受控船舶能够平稳跟随领航船舶，修正由于数据丢失造成的路径误差。结果表明，该方法在高干扰环境下，尤其在通信延迟和数据包丢失的情况下，仍能维持良好的路径跟随性能。与传统的控制方法相比，基于DQN和BC的混合控制策略显著提高了无人水面船舶的跟随精度和系统稳定性，具有较强的鲁棒性，能够在复杂和动态变化的海洋环境中有效运行。更多还原

【Abstract】 In order to solve the problem of controlling the path following of unmanned surface vessels(USVs) in complex marine environments, the article constructs a communication system based on MAVLink to realize the real-time state transmission between the pilot vessel and the controlled vessel, and to ensure that the controlled vessel is able to dynamically adjust according to the real-time position, speed and other information of the pilot vessel.And the learning method of Deep Q-Network(DQN) is utilized to enable the controlled ship to learn the optimal sailing path independently, so as to improve the following accuracy.Under unstable communication conditions, Backstepping Control(BC) is used for state prediction and real-time feedback compensation, which ensures that the controlled vessel can follow the pilot vessel smoothly and corrects the path error caused by data loss.The results show that the method can still maintain good path following performance in high interference environments, especially under communication delay and packet loss.Compared with traditional control methods, the hybrid control strategy based on DQN and BC significantly improves the following accuracy and system stability of unmanned surface vessels with strong robustness, and is able to operate effectively in complex and dynamically changing marine environments.更多还原