LQ decomposition based subspace identification under deterministic type disturbance

Shengnan Zhang, Tao Liu, Jie Hou, Xiongwei Ni

Research output: Contribution to journalArticle

1 Citation (Scopus)
15 Downloads (Pure)

Abstract

To overcome the influence from deterministic type load disturbance with unknown dynamics, a biaseliminated subspace identification method is proposed for consistent estimation. By decomposing the output response into three parts, deterministic, disturbed and stochastic components, in terms of the linear superposition principle, an LQ decomposition approach is developed to eliminate the disturbance and noise effect for unbiased estimation of the deterministic system state. Subsequently, a shift-invariant approach is given to retrieve the state matrices. Consistent estimation on the state matrices is analyzed with a proof. Illustrative example of open-loop system identification is shown to demonstrate the effectiveness and merit of the proposed method.

Original languageEnglish
Pages (from-to)243-251
Number of pages9
JournalSystems Science and Control Engineering
Volume5
Issue number1
DOIs
Publication statusPublished - 1 Jun 2017

Keywords

  • Consistent estimation
  • Kalman innovation form
  • Load disturbance
  • LQ decomposition
  • Subspace identification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization
  • Artificial Intelligence

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