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

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number6748050
Pages (from-to)2156-2166
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number12
DOIs
Publication statusPublished - 2014 Dec 1

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Keywords

  • Data driven
  • low-order linear model
  • nonlinear systems
  • virtual un-modeled dynamics.

ASJC Scopus subject areas

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

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