Optimized support vector regression algorithm-based modeling of ship dynamics |
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| Institution: | 1. Intelligent Transportation Systems Research Center, Wuhan University of Technology, 430063 Hubei, China;2. Computer Science, Carl-von-Ossietzky University of Oldenburg, 26121 Oldenburg, Germany;3. National Engineering Research Center for Water Transport Safety, 430063 Hubei, China;4. Hubei Key Laboratory of Inland Shipping Technology, 430063 Hubei, China;5. Sunwin Intelligent Co., Ltd., 230000 Hefei, China;1. DITEN – Naval Architecture and Marine Engineering Group, University of Genoa, Via Montallegro 1, 16145 Genova, Italy;2. CNR-INSEAN, National Research Council-Marine, Technology Research Institute, Via di Vallerano 139, 00128 Rome, Italy;1. Computer Science, Carl-von-Ossietzky University of Oldenburg, 26121 Oldenburg, Germany;2. School of Navigation, Wuhan University of Technology, 430063 Hubei, China;3. Hubei Key Laboratory of Inland Shipping Technology, 430063 Hubei, China;1. School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China;2. Fujian Province Key Laboratory of Structural Performances in Ship and Ocean Engineering, Fuzhou 350116, China |
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| 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. |
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| Keywords: | Parameter estimation Simplified ship dynamic model Different types of ships Support vector regression algorithm Artificial bee colony algorithm Maritime traffic simulation |
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