Nowadays, tracking control of mobile robots under network circumstances is a fertile topic of great research interest in control and robotics communities. In this paper, an adjustable event-triggered mechanism based on Lyapunov analysis is proposed to reduce the communication burden in the controller-to-robot channel. Besides, a compensation system is introduced to deal with input delays. Unlike other dynamic models of mobile robots that consider torques or voltages as control inputs, the derived dynamic model admits direct commands of desired linear and angular velocities which is highly desirable for commercial mobile robots available in the market. Due to the unavailability of internal parameters of the robot, an adaptive law is designed to estimate them on-line during the operation. The proposed event-triggered adaptive control scheme (ETAC) is experimentally validated on a commercial robot (PatrolBot). The obtained results are consistent with simulations and show a significant saving in bandwidth usage compared to traditional time-triggered implementation.

如今，在网络环境下对移动机器人的跟踪控制是控制和机器人社区中一个具有重大研究兴趣的肥沃课题。在本文中，提出了一种基于李雅普诺夫分析的可调事件触发机制，以减少控制器到机器人通道中的通信负担。此外，还引入了补偿系统来处理输入延迟。与将扭矩或电压作为控制输入的其他移动机器人动态模型不同，派生的动态模型允许直接命令所需的线速度和角速度，这对于市场上可用的商业移动机器人来说是非常理想的。由于机器人内部参数的不可用，设计了一种自适应律来在操作过程中对其进行在线估计。所提出的事件触发自适应控制方案 (ETAC) 在商业机器人 (PatrolBot) 上进行了实验验证。获得的结果与模拟一致，并且与传统的时间触发实现相比，显着节省了带宽使用量。