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Publications
Xu, H.T., Hassani, V., Hinostroza, M.A. and Guedes Soares, C. (2018), “Real-time parameter estimation of nonlinear vessel steering model using support vector machine”, Proceedings of the ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2018), 17-22 June, Madrid, Spain
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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