Tracking control of intelligent ship based on deep reinforcement learning - Kang ZHU (Zhongguo Jianchuan Yanjiu, 2021)
Objectives The tracking control of intelligent ships often faces the problem of low controller stability in complex control environments and manual algorithmic computing. In order to achieve precise tracking control, this paper proposes a controller based on deep reinforcement learning (DRL).MethodsGuided by the line-of-sight (LOS) algorithm and based on the maneuvering characteristics and control requirements of ships, this paper formulates a path of Markov decision processes by following the control problem, designing its state space, action space and reward by applying a deep deterministic policy gradient (DDPG) algorithm to implement the controller. An off-line learning method was used to train the controller. After the training, a comparison was made with BP-PID control to analyze the control effects.ResultsSimulation results show that the deep reinforcement learning (DRL) controller can rapidly converge from the training process to meet the control requirements, with the advantages of small yaw error, and a visible reduction in the frequency of changes of the rudder angle.Conclusions The study results can provide a reference for the tracking control of intelligent ships.