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

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.