Path-Guided Model-Free Flocking Control of Unmanned Surface Vehicles Based on Concurrent Learning Extended State Observers

Abstract

This article addresses the path-guided flocking control of unmanned surface vehicles (USVs) suffering from fully unknown kinetics. A model-free learning and anti-disturbance control method is developed to achieve path-guided flocking without using prior knowledge of model nonlinearities, ocean disturbances, or control input gains. Specifically, data-driven concurrent learning extended state observers (CLESOs) based on fuzzy systems are presented to estimate the unknown kinetics of USVs. With the proposed CLESO, a model-free path-following control law is proposed for a leader USV to follow a parameterized path. Then, model-free flocking control laws based on potential functions are proposed for follower USVs to avoid collisions and maintain network links within available communication ranges. Through cascade stability analysis, the closed-loop system is proven to be globally asymptotically stable. Simulation results substantiate the proposed CLESO-based anti-disturbance control approach for path-guided flocking of a swarm of USVs.