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.


    Access

    Download


    Export, share and cite



    Title :

    Tracking control of intelligent ship based on deep reinforcement learning


    Contributors:
    Kang ZHU (author) / Zhen HUANG (author) / Xuming WANG (author)


    Publication date :

    2021




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Ship Recognition and Tracking System for Intelligent Ship Based on Deep Learning Framework

    Bo Liu / Sheng Zheng Wang / Z.X. Xie et al. | DOAJ | 2019

    Free access

    Deep Reinforcement Learning Based Tractor-Trailer Tracking Control

    Kang, Qi / Hartmannsgruber, Andreas / Tan, Sze-Hui et al. | IEEE | 2024


    Intelligent Control of Manipulator Based on Deep Reinforcement Learning

    Zhou, Jiangtao / Zheng, Hua / Zhao, Dongzhu et al. | IEEE | 2021


    Signal lamp intelligent control method based on deep reinforcement learning

    WEI KAI / ZHU YONG / ZHANG DONGHAI et al. | European Patent Office | 2024

    Free access

    Target Tracking Control of UAV Through Deep Reinforcement Learning

    Ma, Bodi / Liu, Zhenbao / Zhao, Wen et al. | IEEE | 2023