High-gain observer-based model predictive control for cross tracking of underactuated autonomous Underwater Vehicles: A comparative study

Abstract

In this paper, a disturbance observer-based model predictive control (DO-MPC) scheme is developed for cross tracking of underactuated autonomous underwater vehicles (AUVs) under sea current disturbances. A high-gain observer is used to estimate the current velocity, external sway force and yaw torque. Based on the disturbance estimates, a nonlinear model predictive controller is designed with consideration of actuator constraints. The control inputs are solved by optimizing the future trajectories of the nonlinear system under input constraints within a certain time horizon, which are predicted by the system model with estimated disturbances. The stability of the predictive control cross-tracking system is also proved with a Lyapunor-based method. The comparative simulation results with different algorithms are provided to validate the effectiveness of the proposed method.