Online non-parametric 4 DOF modelling of ship motion using hybrid kernel relevance vector machine
Year: 2025
Type: article
Abstract: The complex characteristics of ship motion under various manoeuvres and environmental conditions are determined online by a non-parametric identification model. This paper introduces a novel online ship motion modelling method that adaptively adjusts the training samples based on a sparse hybrid kernel relevance vector machine. Firstly, to fully capture the motion characteristics of the ship, the manoeuvrability test data is collected from a marine manoeuvring simulator, including different operators, engine orders, rudder angles and environments. Additionally, full-scale sea trial data is obtained from the YUKUN vessel, a ship type different from the virtual ship in the simulator. Subsequently, an adaptive criterion for updating the training sample is developed, and a sliding time window is applied to dynamically update the ship motion data. Finally, the motion states and trajectories of both ships are predicted under different conditions. These results show that the proposed method is well-suited for online non-parametric modelling of different ships and manoeuvre conditions. The proposed scheme's mean squared error and mean absolute error are below 0.10 and 0.16. Compared with the non-adaptive online scheme, the proposed approach has stronger generalisation and adaptability. This scheme can serve as a reference for online non-parametric modelling of unmanned surface vehicles .
Source: Ocean Engineering
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,150
OpenAlex ID: W4410216789