Intelligent ship anti-rolling control system based on a deep deterministic policy gradient algorithm and the Magnus effect
Journal article, 2022

Anti-rolling devices are widely used in modern shipboard components. In particular, ship anti-rolling control systems are developed to achieve a wide range of ship speeds and efficient anti-rolling capabilities. However, factors that are challenging to solve accurately, such as strong nonlinearities, a complex working environment, and hydrodynamic system parameters, limit the investigation of the rolling motion of ships at sea. Moreover, current anti-rolling control systems still face several challenges, such as poor nonlinear adaptability and manual parameter adjustment. In this regard, this study developed a dynamic model for a ship anti-rolling system. In addition, based on deep reinforcement learning (DRL), an efficient anti-rolling controller was developed using a deep deterministic policy gradient (DDPG) algorithm. Finally, the developed system was applied to a ship anti-rolling device based on the Magnus effect. The advantages of reinforcement learning adaptive control enable controlling an anti-rolling system under various wave angles, ship speeds, and wavelengths. The results revealed that the anti-rolling efficiency of the intelligent ship anti-rolling control method using the DDPG algorithm surpassed 95% and had fast convergence. This study lays the foundation for developing a DRL anti-rolling controller for full-scale ships.

Author

Lin Jianfeng

Harbin Engineering University

Chalmers, Mechanics and Maritime Sciences (M2), Fluid Dynamics

Yang Han

Harbin Engineering University

Chunyu Guo

Harbin Engineering University

Yumin Su

Harbin Engineering University

Ruofan Zhong

Harbin Engineering University

Physics of Fluids

1070-6631 (ISSN) 1089-7666 (eISSN)

Vol. 34 5 057102

Subject Categories

Vehicle Engineering

Control Engineering

Computer Systems

DOI

10.1063/5.0089697

More information

Latest update

5/25/2022