文章基本信息

标题：Convergence for SISO ILC Systems with Locally Lipschitz Nonlinear Dynamics
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作者：Deyuan Meng ; Kevin L. Moore
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：12083-12088
DOI：10.1016/j.ifacol.2017.08.2156
语种：English
出版社：Elsevier
摘要：AbstractThis paper is devoted to iterative learning control (ILC) for single-input single-output (SISO), affine nonlinear systems with locally Lipschitz dynamics and subject to iteration-varying uncertainties arising from external disturbances and initial state shifts. By adopting a P-type update law, a necessary and sufficient condition is proposed to ensure the convergence of nonlinear SISO ILC. It is shown that the ILC process converges robustly with the final error bound depending continuously upon the bounds of iteration-varying uncertainties. Simulations illustrate the validity of the convergence results.
关键词：KeywordsIterative learning controlnonlinear systemslocally Lipschitz condition