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...
Published in: | Journal of Offshore Mechanics and Arctic Engineering |
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Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
ASME Press
2019
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Subjects: | |
Online Access: | http://hdl.handle.net/11250/2640408 https://doi.org/10.1115/1.4043806 |
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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