Real-Time Parameter Estimation of Nonlinear Vessel Steering Model Using 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, manoeuvring 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: | Volume 11B: Honoring Symposium for Professor Carlos Guedes Soares on Marine Technology and Ocean Engineering |
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| Main Authors: | , , , |
| Format: | Book Part |
| Language: | English |
| Published: |
ASME
2018
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| Subjects: | |
| Online Access: | http://hdl.handle.net/11250/2593423 https://doi.org/10.1115/OMAE2018-78234 |
| 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, manoeuvring 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, 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. publishedVersion Copyright © 2018 by ASME |
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