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Dynamic system identification of underwater vehicles using multi-output Gaussian processes

Version 2 2025-07-03, 03:46
Version 1 2023-05-21, 00:54
journal contribution
posted on 2025-07-03, 03:46 authored by W Ariza Ramirez, J Kocijan, Zhi Quan LeongZhi Quan Leong, Hung NguyenHung Nguyen, SG Jayasinghe
Non-parametric system identification with Gaussian processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle (AUV) dynamics with a low amount of data. Multi-output Gaussian processes and their aptitude for modelling the dynamic system of an underactuated AUV without losing the relationships between tied outputs are used. The simulation of a first-principle model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom (DoF) is also shown in this paper. Multi-output Gaussian processes compared with the popular technique of recurrent neural network show that multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing the measurement of confidence.

History

Publication title

International Journal of Automation and Computing

Volume

18

Issue

5

Pagination

681-693

ISSN

1476-8186

Department/School

National Centre for Maritime Engineering and Hydrodynamics

Publisher

Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature

Publication status

  • Published

Place of publication

China

Rights statement

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2021

Socio-economic Objectives

270401 Autonomous water vehicles, 280110 Expanding knowledge in engineering