Double Deep Q Network Optimized Linear Active Disturbance Rejection Control for Ship Course Keeping

Yuemin Zheng, Zhongxin Liu, Jin Tao*, Qinglin Sun, Hao Sun, Mingwei Sun, Zengqiang Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


This paper mainly proposes a parameter-optimized linear active disturbance rejection controller (LADRC) based on a double deep Q network (DDQN) and applies it to ship course control. Firstly, based on the separate mathematical models’ equation, a ship’s dynamic model is established. Then, a LADRC based course keeping controller is designed to overcome the ship’s environmental disturbances and internal uncertainty during navigation. Furthermore, to facilitate LADRC parameter adjustment and obtain a better performance of ship course keeping control, the DDQN is applied to tune the adaptive parameters of LADRC. Finally, simulation results and comparisons on ship course keeping show that the proposed DDQN optimized LADRC can control the ship’s heading angle to track the planned course, and the control performance outperforms the traditional LADRC.

Original languageEnglish
Title of host publicationProceedings of 2021 Chinese Intelligent Systems Conference
EditorsYingmin Jia, Weicun Zhang, Yongling Fu, Zhiyuan Yu, Song Zheng
Number of pages16
ISBN (Electronic)978-981-16-6328-4
ISBN (Print)978-981-16-6327-7
Publication statusPublished - 2022
MoE publication typeA4 Conference publication
EventChinese Intelligent Systems Conference - Fuzhou, China
Duration: 16 Oct 202117 Oct 2021
Conference number: 17

Publication series

NameLecture Notes in Electrical Engineering
Volume803 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


ConferenceChinese Intelligent Systems Conference
Abbreviated titleCISC


  • Double deep Q network
  • Linear active disturbance rejection control
  • Parameter optimization
  • Reinforcement learning
  • Ship course keeping control


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