Proceedings of the International Conference Recent Advances in Materials, Processes and Technology for Sustainability (RAMPTS 2025)

<Previous Article In Volume
Next Article In Volume>

Adaptive sliding mode control with Gaussian process regression for trajectory tracking of remotely operated underwater vehicles

Authors
S. Sidharthan1, *, Nandana Sajeevan2, R. Devapriya2, Santhakumar Mohan1
1Department of Mechanical Engineering, Indian Institute of Technology, Palakkad, India
2Department of Electrical and Electronics Engineering, College of Engineering, Trivandrum, India
*Corresponding author. Email: 132414009@smail.iitpkd.ac.in
Corresponding Author
S. Sidharthan
Available Online 25 December 2025.
DOI
10.2991/978-94-6463-922-3_20How to use a DOI?
Keywords
Remotely operated underwater vehicle (ROV); Adaptive sliding mode control (AMSC); Gaussian process regression (GPR); Lyapunov’s theory
Abstract

Marine robotics refers to the class of autonomous and unmanned marine vehicles operating with minimal human intervention. The marine aquatic environment consists of uncertainties and disturbances that affect the operational efficiency of marine robotic vehicles. Remotely operated underwater vehicles (ROVs) are designed to operate under the surface of water bodies, applications of which include monitoring, marine surveys, and inspection of marine vessels and structures. To ensure the effective operation of ROVs, accurate trajectory tracking in the presence of disturbances and uncertainties is essential. Nonlinear control algorithms such as sliding mode control (SMC), backstepping control and model predictive control (MPC) proved their effectiveness in controlling the trajectory tracking of ROVs. The advantage of the proposed adaptive sliding mode control (ASMC) enhanced with Gaussian process regression (GPR) is the reduced control activity and thereby reducing the thruster energy requirement. In this study, the effectiveness of the proposed control scheme in trajectory tracking is proved with simulations and the control effort is compared with established control algorithms such as SMC and integral SMC. The stability of the ASMC + GPR algorithm is proved using Lyapunov’s theory.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the International Conference Recent Advances in Materials, Processes and Technology for Sustainability (RAMPTS 2025)
Series
Atlantis Highlights in Material Sciences and Technology
Publication Date
25 December 2025
ISBN
978-94-6463-922-3
ISSN
2590-3217
DOI
10.2991/978-94-6463-922-3_20How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - S. Sidharthan
AU  - Nandana Sajeevan
AU  - R. Devapriya
AU  - Santhakumar Mohan
PY  - 2025
DA  - 2025/12/25
TI  - Adaptive sliding mode control with Gaussian process regression for trajectory tracking of remotely operated underwater vehicles
BT  - Proceedings of the International Conference Recent Advances in Materials, Processes and Technology for Sustainability (RAMPTS 2025)
PB  - Atlantis Press
SP  - 299
EP  - 311
SN  - 2590-3217
UR  - https://doi.org/10.2991/978-94-6463-922-3_20
DO  - 10.2991/978-94-6463-922-3_20
ID  - Sidharthan2025
ER  -