Event-Triggered Adaptive Finite-Time Control for a Robotic Manipulator System With Global Prescribed Performance and Asymptotic Tracking
针对受扰机器人操作手系统，提出一种动态事件触发自适应有限时间跟踪控制方案，通过新预设性能函数消除初始条件限制，并保证跟踪误差渐近收敛到零，节省通信资源。
This article studies the dynamic event-triggered adaptive finite-time tracking control issue for a robotic manipulator (RM) system with disturbances. First, a new global prescribed performance function (PPF) is designed based on a scaling function such that the tracking error evolves within the constrained bounds and the restriction related to the initial conditions is removed. Then, the finite-time command filter (FTCF) is used to avoid the direct derivations of virtual controllers and the singularity issue of the conventional backstepping technique. Moreover, the filtering errors caused by the FTCF are removed by the designed error compensation mechanism. A novel dynamic event-triggered mechanism (DETM) using the dynamic auxiliary variable is designed to save communication resources. The proposed control scheme can guarantee that all signals of the RM are globally bounded within a finite time, and the tracking error can asymptotically reach zero. Finally, a simulation example and several comparative simulations show the validity of the proposed scheme.