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  • 标题:Adaptive Output Regulation via Nonlinear Luenberger Observers ⁎
  • 本地全文:下载
  • 作者:Michelangelo Bin ; Pauline Bernard ; Lorenzo Marconi
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2019
  • 卷号:52
  • 期号:16
  • 页码:580-585
  • DOI:10.1016/j.ifacol.2019.12.024
  • 语种:English
  • 出版社:Elsevier
  • 摘要:In Marconi et al. 2007, it was shown that a solution to the output regulation problem for minimum-phase normal forms always exists. That approach, however, yields only an existence result, and no general analytic procedure is known to actually choose the regulator’s degrees of freedom, even for simple problems. In this paper we propose an adaptive regulator that, leveraging the aforementioned existence result, self-tunes online according to an optimization policy. To this aim, the regulator may employ every system identification scheme that fulfills some given strong stability properties, and the asymptotic regulation error is proved to be directly related to the prediction capabilities of the identifier.
  • 关键词:KeywordsOutput RegulationAdaptive Output RegulationIdentification for Control
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