Real-Time Parameter Estimation of a Nonlinear Vessel Steering Model Using a Support Vector Machine

The least-square support vector machine (LS-SVM) is used to estimate the dynamic parameters of a nonlinear marine vessel steering model in real-time. First, maneuvering tests are carried out based on a scaled free-running ship model. The parameters are estimated using standard LS-SVM and compared wi...

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Bibliographic Details
Published in:Journal of Offshore Mechanics and Arctic Engineering
Main Authors: Xu, Haitong, Hinostroza, M. A., Hassani, Vahid, Guedes Soares, C
Format: Article in Journal/Newspaper
Language:English
Published: ASME Press 2019
Subjects:
Parameter estimation
Ships
Support vector machines
Vessels
Arctic
Online Access:http://hdl.handle.net/11250/2640408
https://doi.org/10.1115/1.4043806
Description
Summary:The least-square support vector machine (LS-SVM) is used to estimate the dynamic parameters of a nonlinear marine vessel steering model in real-time. First, maneuvering tests are carried out based on a scaled free-running ship model. The parameters are estimated using standard LS-SVM and compared with the theoretical solutions. Then, an online version, a sequential least-square support vector machine, is derived and used to estimate the parameters of vessel steering in real-time. The results are compared with the values estimated by standard LS-SVM with batched training data. By comparison, a sequential least-square support vector machine can dynamically estimate the parameters successfully, and it can be used for designing a dynamic model-based controller of marine vessels. acceptedVersion

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