An intelligent course keeping active disturbance rejection controller based on double deep Q-network for towing system of unpowered cylindrical drilling platform

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

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)
72 Downloads (Pure)

Abstract

Towing is a widely used mode of transportation in offshore engineering, and towing of unpowered platforms is of particular significance. However, the addition of unpowered facilities has increased the difficulty of ship maneuvering. Moreover, the marine environment is complex and changeable, and sudden winds or waves can have unpredictable effects on the towing process. Therefore, it is of great significance to overcome the influence of the harsh marine environment while navigating the towing system following a planned course to a target sea area. To tackle the time-varying disturbances, a course control method for a towing system of unpowered cylindrical drilling platform is designed based on double deep Q-network (DQN) optimized linear active disturbance rejection control (LADRC). To be specific, to tackle the difficulty of LADRC tuning, double DQN is applied to select the best parameters of the LADRC at any time according to the states of the system, without relying on the specific information of the model and the controller. The course control performance of the towing system is evaluated in a simulation environment under various disturbances. Moreover, the Monte Carlo experiment is used to test the robustness of the controller when the ship's mass changes and the robustness of the proposed method is verified by testing with various heading angles. The results show that the LADRC with adaptive parameters optimized by double DQN performs well under external interference and inherent uncertainty, and compared with the traditional LADRC, the proposed method has better course control effects.

Original languageEnglish
Pages (from-to)8463-8480
Number of pages18
JournalINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
Volume31
Issue number17
Early online date19 Aug 2021
DOIs
Publication statusPublished - 25 Nov 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • double deep Q-network
  • linear active disturbance rejection control
  • reinforcement learning
  • towing system of unpowered cylindrical drilling platform

Fingerprint

Dive into the research topics of 'An intelligent course keeping active disturbance rejection controller based on double deep Q-network for towing system of unpowered cylindrical drilling platform'. Together they form a unique fingerprint.

Cite this