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Volume 10
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10.3390/drones10070498
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Article

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

by
Yuankui Wang
1,2,
Zhiwei Sun
1,*,
Xiange Tian
3,*,
Yuhang Jia
1,
Hao Li
2,
Bohan Wang
1,
Dahai Zhang
1 and
Peng Qian
1
1
State Key Laboratory of Ocean Sensing, Ocean College, Zhejiang University, Zhoushan 316021, China
2
Kunming Branch of the 705 Research Institute, China State Shipbuilding Corporation Limited, Kunming 650032, China
3
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
*
Authors to whom correspondence should be addressed.
Drones 2026, 10(7), 498; https://doi.org/10.3390/drones10070498
Submission received: 30 March 2026 / Revised: 11 May 2026 / Accepted: 26 May 2026 / Published: 30 June 2026
(This article belongs to the Special Issue Advances in Autonomous Underwater Drones: 2nd Edition)
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Abstract

In this paper, a Gaussian process-based model predictive control (GP-MPC) method is proposed, which aims to enhance the trajectory tracking performance of autonomous underwater vehicles (AUVs). This method can compensate for internal errors and external disturbances based on a limited amount of data. Firstly, numerical models of the AUV are presented. Then, the offline GP-MPC algorithm and online GP-MPC algorithm are presented and described. Meanwhile, the current disturbances and initial errors are also considered. The circular trajectory, L-shaped steering trajectory, and lemniscate trajectory are tracked to evaluate the trajectory tracking performances of different algorithms. Compared with proportional–integral–derivative (PID) and nominal MPC algorithms, the GP-MPC algorithms show reduced root mean square error (over 40%) and reduced maximum error (over 40%) in both position and yaw angle when performing different trajectory tracking tasks. Finally, real-time pool experiments are conducted to validate the implementation feasibility of the GP-corrected MPC framework on a physical AUV under surface three-degrees-of-freedom motion, while the online GP-MPC is evaluated through numerical simulations.
Keywords: autonomous underwater vehicle; trajectory tracking; Gaussian process regression; model predictive control autonomous underwater vehicle; trajectory tracking; Gaussian process regression; model predictive control

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MDPI and ACS Style

Wang, Y.; Sun, Z.; Tian, X.; Jia, Y.; Li, H.; Wang, B.; Zhang, D.; Qian, P. Trajectory Tracking Control of Autonomous Underwater Vehicles Using GP-Based Model Predictive Control. Drones 2026, 10, 498. https://doi.org/10.3390/drones10070498

AMA Style

Wang Y, Sun Z, Tian X, Jia Y, Li H, Wang B, Zhang D, Qian P. Trajectory Tracking Control of Autonomous Underwater Vehicles Using GP-Based Model Predictive Control. Drones. 2026; 10(7):498. https://doi.org/10.3390/drones10070498

Chicago/Turabian Style

Wang, Yuankui, Zhiwei Sun, Xiange Tian, Yuhang Jia, Hao Li, Bohan Wang, Dahai Zhang, and Peng Qian. 2026. "Trajectory Tracking Control of Autonomous Underwater Vehicles Using GP-Based Model Predictive Control" Drones 10, no. 7: 498. https://doi.org/10.3390/drones10070498

APA Style

Wang, Y., Sun, Z., Tian, X., Jia, Y., Li, H., Wang, B., Zhang, D., & Qian, P. (2026). Trajectory Tracking Control of Autonomous Underwater Vehicles Using GP-Based Model Predictive Control. Drones, 10(7), 498. https://doi.org/10.3390/drones10070498

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