This note focuses on the leader–follower formation control problem of underactuated surface vehicles (USVs) with the unknown environmental disturbance. Especially, due to the ocean environmental disturbance, the vehicle’s desired-heading angle is altered violently. To address the constraint, a reduced-order extended state observer (ESO) is introduced. The sideslip angle of the vehicle is estimated online to amend the reference heading angle by virtue of the proposed observer. Furthermore, a novel adaptive formation control algorithm is presented to stabilize the dynamic error system. For the merits of the radial basis function neural networks (RBF NNs) and the minimal learning parameter (MLP) techniques, only two adaptive parameters require to be updated online to compensate for the perturbation from the model uncertainty and the environmental disturbance. Based on the proposed controller, it is illustrated that the desired formation pattern can be maintained without the effect of the sideslip angle. Meanwhile, all signals in the closed-loop system are proved to be with the semi-global uniformly ultimately bounded (SGUUB) stability. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm.

本笔记重点讨论未知环境干扰下欠驱动水面车辆 (USV) 的先导-跟随编队控制问题。特别是由于海洋环境扰动，车辆的期望航向角发生剧烈变化。为了解决这个限制，引入了降阶扩展状态观察器（ESO）。车辆的侧滑角在线估计，以通过建议的观察者修改参考航向角。此外，提出了一种新的自适应编队控制算法来稳定动态误差系统。对于径向基函数神经网络 (RBF NN) 和最小学习参数 (MLP) 技术的优点，只需要在线更新两个自适应参数来补偿模型不确定性和环境干扰的扰动。基于所提出的控制器，表明可以在不受侧滑角影响的情况下保持所需的编队模式。同时，证明闭环系统中的所有信号都具有半全局一致最终有界（SGUUB）稳定性。最后，进行数值模拟以证明所提出算法的有效性。