Optimized support vector regression algorithm-based modeling of ship dynamics. (September 2019)

• Record Type: Journal Article
• Authors: Zhu, Man; Hahn, Axel; Wen, Yuan-Qiao; Sun, Wu-Qiang
• Is Part Of: Applied ocean research. Volume 90(2019)
• Publication Date: 2019-09
• Subjects: Parameter estimation -- Simplified ship dynamic model -- Different types of ships -- Support vector regression algorithm -- Artificial bee colony algorithm -- Maritime traffic simulation; Ocean engineering -- Periodicals; 620.416205
• DOI: 10.1016/j.apor.2019.05.027
• Languages: English
• ISSNs: 0141-1187

Abstract

Highlights: A simplistic dynamic model is developed to describe dynamics of different types' ships, which is applicable in the Maritime Traffic Simulation. The structural parameters of LS-SVR are optimized by artificial bee colony algorithm and further assigned with particular settings. The improved LS-SVR is the first time to be used in maritime domain for parameter estimation of ship dynamic models. The approach developed for modeling dynamics of different types' ships is evaluated through simulation study on a container ship and experimental study on an unmanned surface vessel. Abstract: This study contributes to solving the problem of how to derive a simplistic model feasible for describing dynamics of different types of ships for maneuvering simulation employed to study maritime traffic and furthermore to provide ship models for simulation-based engineering test-beds. The problem is first addressed with the modification and simplification of a complicated and nonlinearly coupling vectorial representation in 6 degrees of freedom (DOF) to a 3 DOF model in a simple form for simultaneously capturing surge motions and steering motions based on several pieces of reasonable assumptions. The created simple dynamic model is aiming to be useful for different types of ships only with minor modifications on the experiment setup. Another issue concerning the proposed problem is the estimation of parameters in the model through a suitable technique, which is investigated by using the system identification in combination with full-scale ship trail tests, e.g., standard zigzag maneuvers. To improve the global optimization ability of support vector regression algorithm (SVR) based identification method, the artificial bee colony algorithm (ABC) presenting superior optimization performance with the advantage of few control parameters is used to optimize and assign the particular settings for structural parameters of SVR. Afterward, the simulation study on identifying a simplified dynamic model for a large container ship verifies the effectiveness of the optimized identification method at the same time inspires special considerations on further simplification of the initially simplified dynamic model. Finally, the further simplified dynamic model is validated through not only the simulation study on a container ship but also the experimental study on an unmanned surface vessel so-called I-Nav-II vessel. Either simulation study results or experimental study results demonstrate a valid model in a simple form for describing the dynamics of different types' ships and also validate the performance of the proposed parameter estimation method.