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This research aims to enhance trajectory tracking performance in autonomous underwater vehicles using a Gaussian process-based model predictive control method.

July 2, 2026DronesOpen Access

Trajectory Tracking Control of Autonomous Underwater Vehicles Using GP-Based Model Predictive Control

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This research aims to enhance trajectory tracking performance in autonomous underwater vehicles using a Gaussian process-based model predictive control method.
Presented numerical models of AUVs
Developed offline and online GP-MPC algorithms
Conducted real-time pool experiments and numerical simulations
GP-MPC algorithms reduced root mean square error by over 40% compared to PID and nominal MPC algorithms
Maximum error in position and yaw angle decreased by over 40% with GP-MPC techniques
Demonstrated implementation feasibility on a physical AUV under surface three-degrees-of-freedom motion

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Implication

Randomized trial evaluates trajectory tracking performance in autonomous underwater vehicles, suggesting improved control methods.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a45fffc9ed134303131032a https://doi.org/10.3390/drones10070498

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Trajectory Tracking Control of Autonomous Underwater Vehicles Using GP-Based Model Predictive Control

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Discussion

Implication

Cite This Study

Trajectory Tracking Control of Autonomous Underwater Vehicles Using GP-Based Model Predictive Control

Key Points

Discussion

Implication

Cite This Study

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