文章基本信息

标题：Contraction Analysis of Nonlinear Iterative Learning Control
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作者：Felix H. Kong ; Ian R. Manchester
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2017
卷号：50
期号：1
页码：10876-10881
DOI：10.1016/j.ifacol.2017.08.2444
语种：English
出版社：Elsevier
摘要：AbstractIterative learning control (ILC) is widely used as a simple method for precise tracking of systems under repetitive conditions. ILC operates by “learning” from the previous iteration’s errors, correcting them over a number of iterations. However, the question of whether or not a nonlinear ILC system converges is still in general an open one. Assuming a state-space formulation, we use contraction analysis to formulate a convergence condition for ILC system as a linear matrix inequality (LMI). Finally, we compute a convergence certificate for a simple example involving “anticogging” a permanent-magnet synchronous motor driving a pendulum in simulation.
关键词：Keywordsiterative learning controlcontraction analysislinear matrix inequalities