Boundary-locked event-triggered mechanism-based adaptive flocking control for multi-USV systems

Unmanned surface vehicle swarms (USVS), comprising multiple unmanned surface vehicles (USVs), serve as critical platforms for a wide range of demanding marine operations. However, coordinating USVS poses challenges relating to flock cohesion and communication, as well as the adverse effects of uncertainty. Within this context, this paper introduces an event-based adaptive flocking control law for USVS to address these issues. Firstly, an integrated dual-mode potential function, combining a novel artificial potential function (APF) and a directionally consistent potential function (DCPF), is designed to enhance flocking cohesion while guaranteeing connectivity. Then, a boundary-locked event-triggered (BLET) mechanism effectively reduces the communication load with computable minimum inter-event time (MIET) and maximum triggering interval time (MTIT). Finally, a reinforcement learning-based echo state network (RLESN) is incorporated to compensate for unmodeled dynamics and external disturbances, thereby significantly improving the system's robustness. Simulation results and comparative analyses validate the effectiveness and advantages of the proposed methodology.