Event-Sampled Adaptive Neural Course Keeping Control for USVs Using Intermittent Course Data

This paper addresses the issue of course keeping control (CKC) for unmanned surface vehicles (USVs) under network environments, where various challenges, such as resource constraints and discontinuities yaw caused by data transmission, are taken into account. To tackle constraints, an event-sampled scheme is developed to obtain data, a novel adaptive neural-network-based state observer (NN–SO) achieve reconstruction discontinuous yaw. Using backstepping design method, mechanism, NN–SO, neural output feedback (ANOF) law designed, dynamic technique introduced solve intermission data. Moreover, event-triggered mechanism (ETM) established in controller–actuator (C–A) channel dual-channel (ETANOFC) solution proposed. The theoretical results show that all signals closed-loop system (CLCS) bounded. effectiveness verified through numerical simulations.