Abstract:

This paper presents a finite-time adaptive iterative learning control for robotic systems under arbitrary initial state error. With the concept of initial rectified attractor being introduced, an error variable with an initial rectify term is constructed. The constant robotic system and time-varying robotic system are respectively considered. Based on Lyapunov-like function, accordingly, iterative learning controllers are designed for handling the uncertainties. By applying the unsaturated/ saturated learning mechanisms, the error variable would uniformly converge to zero over the entire time  interval as iteration increase, thereby the  tracking error would achieve practical complete tracking  over a pre-specified interval, while the uniform boundness of all variable in the closed-loop system can be guaranteed. Effectiveness of the proposed control method is demonstrated by numerical simulation.

Key words: iterative learning control, finite-time control, robotic systems, time-varying systems

摘要：

研究任意初态下，机器人系统的有限时间自适应迭代学习控制方法。引入初始修正吸引子的概念，构造一个含有初始修正项的误差变量。针对定常机器人系统和时变机器人系统，采用Lyapunov-like方法，分别设计迭代学习控制器处理系统中不确定性。并且，采用未含/含限幅学习机制，保证闭环系统各变量的一致有界性和误差变量在整个作业区间一致收敛性。藉以实现跟踪误差在预先指定区间的完全跟踪。仿真结果验证所设计控制方法的有效性。

关键词: 迭代学习控制, 有限时间控制, 机器人系统, 时变系统