Depth Control of Underwater Robots Using Sliding Modes and Gaussian Process Regression - UTU Research Portal

A4 Refereed article in a conference publication

Depth Control of Underwater Robots Using Sliding Modes and Gaussian Process Regression

Authors: Lima Gabriel S., Bessa Wallace M., Trimpe Sebastian

Editors: Tiago Pereira do Nascimento, Esther Luna Colombini, Alisson Vasconcelos de Brito, Luciane Terra dos Santos Garcia, Sarah Thomaz de Lima Sá, Luiz Marcos Garcia Gonçalves

Conference name: Latin American Robotics Symposium

Publication year: 2018

Journal: Latin American Robotics Symposium

Book title : Proceedings of the 2018 Latin American Robotics Symposium (LARS)

Series title: Latin American Robotics Symposium

First page : 8

Last page: 12

ISBN: 978-1-5386-7762-9

eISBN: 978-1-5386-7761-2

ISSN: 2639-1775

DOI: https://doi.org/10.1109/LARS/SBR/WRE.2018.00012

Web address : https://ieeexplore.ieee.org/document/8588519

Abstract

The development of accurate control systems for underwater robotic vehicles relies on the adequate compensation for hydrodynamic effects. In this work, a new robust control scheme is presented for remotely operated underwater vehicles. In order to meet both robustness and tracking requirements, sliding mode control is combined with Gaussian process regression. The convergence properties of the closed-loop signals are analytically proven. Numerical results confirm the stronger improved performance of the proposed control scheme.