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arXiv · 2006.02194

Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes

Wilmer Ariza Ramirez·Jus Kocijan·Zhi Leong·Hung Nguyen·Shantha Gamini Jayasinghe
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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 low amount of data. Multi-output Gaussian processes and its aptitude to model the dynamic system of an underactuated AUV without losing the relationships between tied outputs is used. The simulation of a first-principles 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 are 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 a measurement of confidence.

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Wilmer Ariza Ramirez, Jus Kocijan, Zhi Leong, Hung Nguyen, Shantha Gamini Jayasinghe. 2020-06-03. Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes. https://doi.org/10.1007/s11633-021-1308-x

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