A novel estimation algorithm based on data and low-order models for virtual unmodeled dynamics

Yajun Zhang, Tianyou Chai, Jing Sun, Xinkai Chen, Hong Wang

研究成果: Article

4 引用 (Scopus)

抄録

In this paper, the challenging issue of estimating virtual unmodeled dynamics is addressed. A novel estimation algorithm based on historical data and the output of low-order approximation models for virtual un-modeled dynamics is presented. In particular, the virtual un-modeled dynamics are decomposed into known and unknown parts, where only the unknown part is to be estimated. The method effectively avoids the need to use the unknown control input directly, and enables the estimation of the un-modeled dynamics with a relatively simple algorithm. Moreover, it is shown that the proposed algorithm overcomes the difficulty in obtaining the control solutions caused by the fact that the controller input is embedded in un-modeled dynamics. Finally, simulation studies are presented to demonstrate the effectiveness of the proposed method.

元の言語English
記事番号6748050
ページ(範囲)2156-2166
ページ数11
ジャーナルIEEE Transactions on Neural Networks and Learning Systems
25
発行部数12
DOI
出版物ステータスPublished - 2014 12 1

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Software

これを引用

A novel estimation algorithm based on data and low-order models for virtual unmodeled dynamics. / Zhang, Yajun; Chai, Tianyou; Sun, Jing; Chen, Xinkai; Wang, Hong.

:: IEEE Transactions on Neural Networks and Learning Systems, 巻 25, 番号 12, 6748050, 01.12.2014, p. 2156-2166.

研究成果: Article

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