Research on Path-Following Technology of a Single-Outboard-Motor Unmanned Surface Vehicle Based on Deep Reinforcement Learning and Model Predictive Control Algorithm

Abstract

Path following is one of the key technologies for unmanned surface vehicles (USVs). This paper proposes a path-tracking control method for a single-outboard-motor USV based on a Deep Deterministic Policy Gradient (DDPG) algorithm and model predictive control (MPC) algorithm. Initially, the motion model and outboard motor model of the USV are analyzed. Subsequently, simulation and real ship experiments provide a comprehensive performance comparison between the proposed DDPG-MPC method and the traditional ALOS-PID method. The results indicate that for straight path tracking, the DDPG-MPC algorithm achieves 37% and 21% reductions in the average cross error and heading angle error, respectively, compared to the ALOS-PID algorithm. The real ship experiments further validate the DDPG-MPC algorithm’s advantages in real-world environments. Specifically, under disturbances like wind, waves, and currents, the maximum cross error of the DDPG-MPC algorithm is one-third of the ALOS-PID algorithm. Additionally, the DDPG-MPC algorithm sustains a higher and more stable longitudinal velocity over extended periods, while the ALOS-PID algorithm shows greater instability and variability. Overall, the findings confirm the feasibility and effectiveness of the proposed approach, highlighting its potential for enhancing path-tracking control performance in single-outboard-motor USVs.

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