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



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: | |
Item Statistics: | View statistics for this item |
Actions (login required)
![]() |
Item Control Page |