Incremental Sparse Gaussian Process-Based Model Predictive Control for Trajectory Tracking of Unmanned Underwater Vehicles

作     者：Dang, Yukun Huang, Yao Shen, Xuyu Zhu, Daqi Chu, Zhenzhong 

作者机构：Univ Shanghai Sci & Technol Dept Mech Engn Shanghai 200093 Peoples R China 

出 版 物：《IEEE ROBOTICS AND AUTOMATION LETTERS》 (IEEE Robot. Autom.)

年 卷 期：2025年第10卷第3期

页      面：2327-2334页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：Natural Science Foundation of China [U2006228  52171313  62033009] 

主　　题：Mathematical models Training Kernel Uncertainty Trajectory Trajectory tracking Control systems Kinematics Gaussian processes Adaptation models Marine robotics model learning for control motion control 

摘      要：In this letter, a Model Predictive Control (MPC) approach based on the Incremental Sparse Gaussian Process (ISGP) is designed for trajectory tracking of Unmanned Underwater Vehicles (UUVs). The performance of MPC depends on the accuracy of system modeling. However, building an accurate dynamic model for the UUV is challenging due to imprecise hydrodynamic coefficients and strong nonlinearities. Thus, the Gaussian Process (GP) is employed to regress the deviating parts of the system model. A sparsification rule is proposed to reduce the training dataset size by removing less valuable data, thereby simplifying the complexity of GP regression training. Additionally, a method for incrementally updating the training data is provided, along with a rigorous stability proof. Finally, simulations are conducted in a third-party ROS environment to demonstrate the efficiency and accuracy of the proposed method.