Event-Triggered Path-Following Control for Autonomous Surface Vehicles Subject to Stochastic Noise

This article proposes a stochastic event-triggered path-following control scheme aimed at enabling autonomous surface vehicles (ASVs) to track a reference path in a communication-efficient manner. First, a stochastic model of ASV dynamics is established, explicitly incorporating environmental disturbances as additive stochastic noise. Within the line-of-sight (LOS) guidance framework, path-following errors relative to the reference path are subsequently formulated, which incorporate the reference speed, the reference yaw angle, and the path variable. Third, the dynamic event-triggered mechanisms (ETMs) are introduced, based on which kinetic controllers are designed to ensure that the ASV tracks the reference speed and yaw angle accurately. Theoretical analysis using Lyapunov-based stochastic stability criteria demonstrates that both path-following errors and tracking errors are asymptotically bounded in the fourth moment sense, while the occurrence of Zeno behavior can be excluded almost surely. Finally, simulations and experimental validations demonstrate the scheme’s effectiveness, showing a significant reduction in communication load compared with time-triggered baselines.