Design of Ship Trajectory Control Method Integrating Self Disturbance Rejection and Neural Network

Abstract

To reduce the ship trajectory control difficulty, a ship trajectory control method based on active disturbance rejection control and radial basis function neural network is designed. Firstly, a separated model structure is proposed to model the ship’s navigation system, followed by the introduction of active disturbance rejection control technology to control the ship’s trajectory, and the fusion of radial basis function to improve the parameter adjustment effect. It was found that on the AIS and MSSIS datasets when the system was iterated 25 and 22 times, respectively, the fitness values of the research method were as high as 99.46 and 99.51. In addition, when the accuracy of all algorithms was 0.900, the recall rate of the research method was significantly the highest, at 0.752. When the recall rate was 0.900, the accuracy of the research method was significantly the highest, reaching 0.869. In practical applications, when there was no external interference, the heading was a square wave signal, and when the time reached 90.11, the proposed method operated with a small difference between the planned heading and the actual heading. In addition, it was found that the control effect of the research method on ship operation remains highly stable when external interference is added during ship operation. The above results indicate that the designed method has significant advantages in ship trajectory control tasks and can effectively enhance the navigation safety and stability of ships.

Keywords

• active disturbance rejection control
• ships
• track control
• RBF neural network
• motion modeling