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Depth Control of Underwater Robots using Sliding Modes and Gaussian Process Regression
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.
@inproceedings{lima-lars-18, title = {Depth Control of Underwater Robots using Sliding Modes and Gaussian Process Regression}, booktitle = {Proceeding of the 15th Latin American Robotics Symposium}, 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.}, address = {João Pessoa, Brazil}, month = nov, year = {2018}, slug = {lima-lars-18}, author = {Lima, Gabriel S. and Bessa, Wallace M. and Trimpe, Sebastian}, month_numeric = {11} }