Abstract In this paper, an adaptive visual tracking controller is proposed for the nonholonomic mobile robot with an onboard monocular camera, wherein both the velocity and acceleration saturation constraints can be guaranteed by properly selecting the control parameters during the visual servoing process. Specifically, the virtual velocity-level controller is firstly designed based on the first-order filter, by which the bounds of the virtual velocity signals can be estimated. Then, according to the finite-time control theory, the actual acceleration-level controller is developed to track the virtual velocity input in a finite time. For the unknown distance between the plane of feature points and the origin of the global frame due to the lack of the image depth, an adaptive updating law is designed and also used for the parameter identification. The global asymptotical stability of the closed-loop system is proven using Lyapunov techniques with Barbalat’s lemma. The calculations for the upper bounds of velocity and acceleration signals are given. In addition, some tips for the control gains tuning in practical experiments are summarized. Experimental results are provided to validate the effectiveness of the proposed controller.

中文翻译：

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具有速度和加速度饱和约束的移动机器人视觉跟踪

摘要在本文中，提出了一种具有板载单眼相机的非完整移动机器人的自适应视觉跟踪控制器，其中通过在可视伺服过程中正确选择控制参数，可以保证速度和加速度饱和约束。具体来说，首先基于一阶滤波器设计虚拟速度级控制器，通过它可以估计虚拟速度信号的边界。然后，根据有限时间控制理论，开发实际加速度级控制器以在有限时间内跟踪虚拟速度输入。由于图像深度不足，特征点平面与全局帧原点的距离未知，设计了自适应更新律并用于参数识别。使用带有 Barbalat 引理的 Lyapunov 技术证明了闭环系统的全局渐近稳定性。给出了速度和加速度信号的上限的计算。此外，总结了实际实验中控制增益调整的一些技巧。提供了实验结果来验证所提出的控制器的有效性。