文章基本信息

标题：Adaptive Iterative Learning Control of Robot Manipulators for Friction Compensation
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作者：Richard Lee ; Liting Sun ; Zining Wang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：15
页码：175-180
DOI：10.1016/j.ifacol.2019.11.670
语种：English
出版社：Elsevier
摘要：AbstractIterative learning control (ILC) has been well recognized for its ability to improve the tracking performance of systems that perform repetitive tasks. Its achievable performance, however, can be significantly degraded by the presence of non-repetitive disturbances which vary every iteration. Such may be a case for high-precision robot manipulators which are commonly subject to nonlinear and velocity-dependent friction forces. With a traditional PD-type ILC scheme, as joint velocities change along iterations, friction forces can vary substantially and deteriorate the performance of ILC. To address this problem, we propose an adaptive ILC algorithm, in which an adaptive friction compensation signal is introduced with ILC to adaptively identify the friction model over multiple iterations. Theoretical convergence analysis is provided with simulation verification on a 3-degree-of-freedom (DOF) planar manipulator. Experimental verification is also performed on a 5-DOF robot for silicon wafer handling. The verification results show that the proposed adaptive ILC approach can achieve significantly better tracking performance than traditional ILC methods.
关键词：KeywordsIterative learning controlModel reference adaptive controlNonlinear friction identificationRobots