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Publications
Xu, H.T., Hinostroza, M.A., Hassani, V. and Guedes Soares, C. (2019) , “Real-time parameter estimation of nonlinear vessel steering model using support vector machine”, Journal of Offshore Mechanics and Arctic Engineering, Vol. 141(6), 061606
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.
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