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

Ariza Ramirez, W, Kocijan, J, Leong, ZQ ORCID: 0000-0002-0644-1822, Nguyen, HD ORCID: 0000-0003-0118-8597 and Jayasinghe, SG ORCID: 0000-0002-3304-9455 2021 , 'Dynamic system identification of underwater vehicles using multi-output Gaussian processes' , International Journal of Automation and Computing, vol. 18 , 681–693 , doi: 10.1007/s11633-021-1308-x.

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Abstract

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.

Item Type: Article
Authors/Creators:Ariza Ramirez, W and Kocijan, J and Leong, ZQ and Nguyen, HD and Jayasinghe, SG
Keywords: dynamic system identification, dependent Gaussian processes, machine learning, multi-output Gaussian processes, non-parametric identification, autonomous underwater vehicle (AUV)
Journal or Publication Title: International Journal of Automation and Computing
Publisher: Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature
ISSN: 1476-8186
DOI / ID Number: 10.1007/s11633-021-1308-x
Copyright Information:

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

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