Optimal tracking control using discrete disturbance observer for unmanned surface vessel under unknown disturbances

Abstract

This article proposes an optimal control approach to address the trajectory tracking problem for unmanned surface vessels (USVs) subjected to external disturbances such as wind, waves, and currents. An optimal control method based on the Online Adaptive Dynamic Programming (OADP) algorithm is introduced to minimize energy consumption and enhance the USV's tracking performance. A disturbance observer is employed to estimate and compensate for external disturbances effectively. The OADP algorithm incorporates a neural network layer, which simplifies the structure and improves computational efficiency. Stability analysis is conducted using Lyapunov theory, considering weights and tracking errors. Simulation results demonstrate the effectiveness of the proposed control scheme, showing improvements over sliding mode control (SMC) and online actor-critic (AC) algorithms in trajectory tracking and cost optimization under various environmental conditions.