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System Identification of Vessel Steering with Unstructured Uncertainties by Persistent Excitation Maneuvers

Abstract

System identification of vessel steering associated with unstructured uncertainties is considered in this paper. The initial model of vessel steering is derived by a modified second-order Nomoto model (i.e., nonlinear vessel steering with stochastic state-parameter conditions). However, that model introduces various difficulties in system identification, 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 the system-model unstructured uncertainties can also be separated. Furthermore, 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 approach is carried out, for both models (i.e., full state model and reduced-order state model), resorting to an extended Kalman filter (EKF). As illustrated in the results, the reduced-order model was able to successfully identify the required states and parameters when compared to the full state model in vessel steering under persistent excitation maneuvers. Therefore, the proposed approach can be used in a wide range of system identification applications.

Category

Academic article

Language

English

Author(s)

  • Lokukaluge Prasad Perera
  • Paulo Oliveira
  • Carlos Guedes Soares

Affiliation

  • SINTEF Ocean / Energi og transport
  • University of Lisbon (ULisboa)

Date

08.12.2015

Year

2016

Published in

IEEE Journal of Oceanic Engineering

ISSN

0364-9059

Publisher

IEEE Oceanic Engineering Society

Volume

41

Issue

3

Page(s)

515 - 528

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