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  • 标题:Fixed-Time convergent Adaptive Observer for LTI Systems
  • 本地全文:下载
  • 作者:Juan G. Rueda-Escobedo ; Jaime A. Moreno
  • 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:11639-11644
  • DOI:10.1016/j.ifacol.2017.08.1664
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractIn this note a new adaptive observer for linear time invariant systems is proposed. Without persistency of excitation, asymptotic convergence to the state is provided. If persistency of excitation is present in the system, uniform finite-time convergence to the internal state and parameters is guaranteed. Additionally, the convergence time satisfies a constant upper bound that holds for any initial error, that is, the convergence time does not grow unboundedly with the initial error. In this sense we say that the algorithm provides uniform fixed-time convergence. Simulation examples are used to show this property.
  • 关键词:KeywordsState observersAdaptive algorithmsParameter identification
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