#5987. Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes

August 2026publication date
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Journal’s subject area:
Applied Mathematics;
Modeling and Simulation;
Computer Science Applications;
Control and Systems Engineering;
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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.
Keywords:
autonomous underwater vehicle (AUV); Dependent Gaussian processes; dynamic system identification; multi-output Gaussian processes; non-parametric identification

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