The article proposes a robust adaptive framework for platoon of uncertain unmanned surface vehicles (USVs) subject to stochastic environmental loads. The disturbances induced bywaves, wind, and ocean currents in the kinetics are decomposed into deterministic and stochastic components. The deterministic components can be treated as unknown constants, while stochastic components are regarded as Gaussian random disturbances. The stochastic additive noises are also included in the kinematics which stands for the un-modeled dynamics and uncertainty. A comprehensive model including kinematics and kinetics of each USV agent is then derived as stochastic differential equations including standard Wiener processes. Thus, the problem formulation is much more challenging and practical since both the exogenous disturbances and kinematics states are defined by stochastic differential equations. Dynamic surface control technique, quartic Lyapunov functions synthesis, the projection algorithm, and neural networks are employed in order to guarantee that all the tracking errors are semi-globally uniformly ultimately bounded in probability. Finally, the simulation experiments quantify the effectiveness of proposed approach.

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