Learning-Based Data-Driven Heading Control for Unmanned Surface Vehicles: A Nussbaum-RBF Sliding Mode Approach

Abstract

Owing to unknown hydrodynamic characteristics, unmeasurable external disturbances and uncertain control coefficients, conventional control strategies that rely on precise mechanistic models often suffer from notable performance degradation. To address this problem, a novel data-driven Nussbaum-RBF Sliding Mode Control (NRSMC) strategy is proposed for USV heading control. In the proposed framework, a Radial Basis Function (RBF) neural network is employed as a data-driven approximator to learn unknown nonlinear system dynamics online, avoiding the dependence on accurate prior mathematical models. Based on the online-learned dynamic information, a sliding mode control (SMC) mechanism is developed to enhance robustness against approximation errors and external disturbances, and a boundary layer technique is introduced to alleviate the chattering phenomenon. Furthermore, a Nussbaum function is incorporated to address the unknown control coefficients problem, ensuring system stability without requiring prior knowledge of the control coefficients. The stability of the closed-loop system is rigorously analyzed using Lyapunov theory. Comparative simulation results demonstrate that the proposed NRSMC strategy achieves superior tracking accuracy and robustness compared with the Nussbaum-RBF Adaptive Backstepping Control (NRABC) method. Moreover, field experiments conducted further validate the effectiveness and adaptability of the proposed data-driven control approach.