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  • 标题:Iterative Learning Control for Output Tracking of Systems with Unmeasurable States ⁎
  • 本地全文:下载
  • 作者:Xuefang Li ; Dong Shen
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2020
  • 卷号:53
  • 期号:2
  • 页码:1439-1443
  • DOI:10.1016/j.ifacol.2020.12.1915
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractIn this work, a new design framework of adaptive iterative learning control (ILC) approach for a class of uncertain nonlinear systems is presented. By making use of the closed-loop reference model which works as an observer, the developed adaptive ILC method is able to be adopted to deal with the output tracking problem of nonlinear systems without requiring the measurability of system states. In the system, the uncertainties are formed by the product of unknown parameters and state functions that are also unknown as the system states are not available. In order to facilitate the controller design and convergence analysis, the composite energy function (CEF) method is employed, and the accurate tracking task can be realized successfully. The proposed approach extends CEF-based ILC approach sucessfully to output tracking control of nonlinear systems without requiring the system states information and complicated observer design. The effectiveness of the proposed ILC scheme is verified through an illustrative numerical example.
  • 关键词:KeywordsIterative learning control (ILC)composite energy function (CEF)output trackingconvergence analysis
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