In this article, suffering from unmatched visual-servo uncertainties and unknown dynamics/disturbances, an extreme learning-based monocular visual-servo (ELMVS) scheme is developed for maneuvering an unmanned surface vessel (USV) to reach the desired pose. By virtue of the backstepping philosophy, complex visual-servo unknowns are elaborately encapsulated into lumped nonlinearities, which are further accurately accommodated by devising a single-hidden layer feedforward network based adaptive compensating identifier (SACI). Within the SACI architecture, hidden nodes are completely model free and are randomly generated without tedious learning, and thereby dramatically expediting fast-dynamics identification. Moreover, by exploiting approximation residuals, direct hyperbolic-tangent links between input and output layers are deployed to enhance identification accuracy. Eventually, the Lyapunov synthesis guarantees that the proposed ELMVS scheme can asymptotically render visual-servo errors arbitrarily small while target features can be kept within the field of view. Remarkable performance and superiority is finally demonstrated on a prototype USV.

中文翻译：

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基于极限学习的无人水面舰艇单目视觉伺服

在本文中，由于视觉伺服系统存在不确定性和动态/干扰未知，因此开发了一种基于极端学习的单眼视觉伺服系统（ELMVS），以操纵无人水面舰艇（USV）达到所需姿态。依靠后推原理，复杂的视觉伺服未知因素被精心封装成集总的非线性，通过设计基于单隐藏层前馈网络的自适应补偿标识符（SACI）可以进一步准确地解决这些未知问题。在SACI架构中，隐藏节点完全不受模型影响，并且无需繁琐的学习即可随机生成，从而极大地加快了快速动力学识别的速度。此外，通过利用近似残差，在输入和输出层之间部署直接的双曲线正切链接以提高识别精度。最终，Lyapunov综合保证了所提出的ELMVS方案可以渐近地使视觉伺服误差任意小，而目标特征可以保持在视野内。最终在原型USV上展示了卓越的性能和优越性。