MPC-based 3-D trajectory tracking for an autonomous underwater vehicle with constraints in complex ocean environments

Abstract

This paper presents a novel three-dimension (3-D) underwater trajectory tracking method for an autonomous underwater vehicle (AUV) using model predictive control (MPC). First, the 6-degrees of freedom (DoF) model of a fully-actuated AUV is represented by both kinematics and dynamics. After that, the trajectory tracking control is proposed as an optimization problem and then transformed into a standard convex quadratic programming (QP) problem which can be readily computed online. The practical constraints of the system inputs and states are considered effectively in the design phase of the proposed control strategy. To make the AUV move steadily, the control increments are considered as the system input and optimized. The receding horizon implementation makes the optimal control inputs be recalculated at each sampling instant, which can improve the robustness of the tracking control under the model uncertainties and time-varying disturbances. Simulations are carried out under two different 3-D trajectories to verify the performance of trajectory tracking under random disturbances, ocean current disturbances, and ocean wave disturbances. The simulation results are given to show the feasibility and robustness of the MPC-based underwater trajectory tracking algorithm.