文章基本信息

标题：Parameter Identification of Linear Discrete-Time Systems with Guaranteed Transient Performance ⁎
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作者：Alexey A. Belov ; Romeo Ortega ; Alexey A. Bobtsov 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：1038-1043
DOI：10.1016/j.ifacol.2018.09.057
语种：English
出版社：Elsevier
摘要：AbstractDynamic regressor extension and mixing is a new technique for parameter estimation that has proven instrumental in the solution of several open problems in system identification and adaptive control. A key property of the estimator is that, for linear regression models, it guarantees monotonicity of each element of the parameter error vector that is a much stronger property than monotonicity of the vector norm, as ensured with classical gradient or least-squares estimators. The main result of this paper is to give new techniques for deriving explicit conditions on the exogenously specified reference trajectory to guarantee parameter convergence for a class of linear discrete-time single-input single-output systems. A numerical example is given.
关键词：KeywordsIdentification algorithmsPersistent excitationParameter convergence