Full text not available from this repository.

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
Related URLs:	Google Scholar: Leong, ZQ UTAS Profile: Leong, ZQ Google Scholar: Nguyen, HD UTAS Profile: Nguyen, HD
Item Statistics:	View statistics for this item

Actions (login required)

Item Control Page