Journal Article

Disturbance Observer-Based Model Predictive Control for an Unmanned Underwater Vehicle

Li, Boyang, Wen, Chih-Yung, Jiang, Bailun, Hu, Yang, Han, Jixuan

2024

Overview

This work addresses the motion control problem for a 4-degree-of-freedom unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. A disturbance observer-based control scheme is proposed, which is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the developed EAOB has the ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC is implemented by feeding the estimated disturbances back into the MPC’s prediction model, which forms an effective adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and robot operating system environment. The results show that the proposed method can effectively reject unpredictable disturbances and improve the UUV’s control performance.

• Publisher: MDPI AG
• Subject: Autonomous underwater vehicles / Comparative analysis / Controllers / disturbance observer / Disturbance observers / Dynamic models / dynamic positioning / Dynamical systems / Measuring instruments / Methods / model predictive control (MPC) / Motion control / Neural networks / Nonlinear dynamics / Nonlinear systems / Prediction models / Predictive control / Robust control / Systems stability / trajectory tracking / Underwater vehicles / unmanned underwater vehicle (UUV) / Unmanned underwater vehicles / Vehicles