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Publications
Perera, L.P., Oliveira, P. e Guedes Soares, C. (2016), “System Identification of Vessel Steering with Unstructured Uncertainties by Persistent Excitation Maneuvers”, Journal of Oceanic Engineering – IEEE, Vol. 41(3), pp. 515-528
Abstract System identification of vessel steering associated with unstructured uncertainties is considered in this paper. The initial model of vessel steering is derived based on the modified second order Nomoto model (i.e. nonlinear vessel steering conditions and stochastic state-parameter conditions). However, that model introduces difficulties in the system identification approach, due to the presence of a large number of states and parameters and system nonlinearities. Therefore, partial feedback linearization is proposed to simplify the proposed model, where system-model unstructured uncertainties have also been separated.
Partial feedback linearization reduces the number of states and parameters and the system nonlinearities, given the resulting reduced-order state model. Then, the system identification can be carried out, for both models (full state model and reduced-order state model), resorting to an extended Kalman filter (EKF). As illustrated in the results reported in this work, the reduced-order model in vessel steering successfully identify the required states and parameters when compared to the full state model in vessel steering under Persistent Excitation Maneuvers. The proposed approach can be used in a wide range of system identification applications.
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