Predefined-time repetitive learning control of robotic manipulators
[机械臂预定时间重复学习控制]作者机构：College of Information Engineering Zhejiang University of Technology Hangzhou 310023 China
出 版 物：《Kongzhi yu Juece/Control and Decision》 (Control and Decision)
年 卷 期：2024年第39卷第11期
页 面：3719-3726页
核心收录：
学科分类：08[工学] 080202[工学-机械电子工程] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 0835[工学-软件工程] 080201[工学-机械制造及其自动化]
基 金：国家自然科学基金项目(62222315,61973274) 浙江省自然科学基金项目(LZ22F030007)
主 题：backstepping recursive algorithm periodic uncertainty predefined-time control repetitive learning law robot manipulator tracking control
摘 要：A backstepping-based predefined-time repetitive learning control scheme is proposed for uncertain robot manipulators to achieve rapid and high-precision tracking control performance. A non-singular predefined-time virtual controller is constructed to effectively avoid the singularity issues caused by the differentiation of the virtual controller in conventional finite-time backstepping design. It ensures that the tracking error of the robot manipulators joint positions converges to a neighborhood of the origin within the predefined time. Then, the lumped uncertainty of the manipulator is separated into periodic and non-periodic parts by considering the periodic characteristics of the desired trajectory. A fully saturated repetitive learning law is constructed to accurately estimate and compensate for the periodic uncertainty. Meanwhile, a robust control law is developed and the terminal attracting technique is applied to guarantee the effective compensation of the non-periodic uncertainty including external disturbances, such that the high-precision tracking of the robot manipulators joint positions is achieved. Finally, the stability of the closed-loop system and the error convergence performance of the proposed scheme are analyzed through the Lyapunov stability synthesis. The effectiveness of the proposed control method is verified by comparative simulations. © 2024 Northeast University. All rights reserved.
同方期刊数据库