Adaptive Model Predictive Control for Underwater Manipulators Using Gaussian Process Regression

Abstract

In this paper, the precise control of the underwater manipulator has studied under the conditions of uncertain underwater dynamics and time-varying external interference. An improved adaptive model predictive control (MPC) method is proposed for a multiple-degrees-of-freedom (DOF) underwater manipulator. In this method, the Gaussian process regression (GPR) algorithm has been embedded into the precise trajectory tracking control of the underwater manipulator. The GPR algorithm has been used to predict the water resistance, additional mass, buoyancy and external interference in real time, and the control law has been calculated by the terminal constraint MPC to realize the adaptive internal and external interference compensation. In addition, a more accurate dynamic model of the underwater 6-DOF manipulator is established by combining Lagrange equation with Morrison formula. Finally, the effectiveness of the adaptive MPC using GPR method is verified by a series of comparative simulations.