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

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2021-11-25
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Mcode
Degree programme
Language
en
Pages
18
Series
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
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 modeland 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.
Description
Funding Information: This work was supported by the National Natural Science Foundation of China (Grant No.61973172, 61973175, 62003175, and 62003177), the National Key Research and Development Project (Grant No. 2019YFC1510900), the key Technologies Research and Development Program of Tianjin (Grant No.19JCZDJC32800), this project also funded by China Postdoctoral Science Foundation (Grant No.2020M670633), and Academy of Finland (Grant No.315660). Funding Information: Academy of Finland, 315660; China Postdoctoral Science Foundation, 2020M670633; Key Technologies Research and Development Program of Tianjin, 19JCZDJC32800; National Key Research and Development Project, 2019YFC1510900; National Natural Science Foundation of China, 61973172; 61973175; 62003175; 62003177 Funding information Publisher Copyright: © 2021 The Authors. International Journal of Robust and Nonlinear Control published by John Wiley & Sons Ltd.
Keywords
double deep Q-network, linear active disturbance rejection control, reinforcement learning, towing system of unpowered cylindrical drilling platform
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Citation
Zheng , Y , Tao , J , Sun , Q , Sun , H , Sun , M & Chen , Z 2021 , ' An intelligent course keeping active disturbance rejection controller based on double deep Q-network for towing system of unpowered cylindrical drilling platform ' , INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL , vol. 31 , no. 17 , pp. 8463-8480 . https://doi.org/10.1002/rnc.5740