Parameter identification of a pressure regulator with a nonlinear structure using a particle filter based on the nonlinear state space model

Tsukasa Ishigaki, Tomoyuki Higuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fusion of a simulation model and observation data has been investigated extensively for the purpose of data assimilation in geophysics. The inaccuracy of the parameters, initial conditions, or boundary conditions causes a discrepancy in the simulation results and the actual phenomenon. The present paper describes the parameter identification of a pressure regulator with a nonlinear structure by sequential Bayes estimation in the framework of data assimilation. A damping coefficient of feedback system in the pressure regulator that cannot be observed directly is estimated using a particle filter and a nonlinear state space model. The data assimilation concept is demonstrated using a pressure regulator as an engineering application.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Information Fusion, FUSION 2008
DOIs
Publication statusPublished - 2008 Dec 1
Externally publishedYes
Event11th International Conference on Information Fusion, FUSION 2008 - Cologne, Germany
Duration: 2008 Jun 302008 Jul 3

Publication series

NameProceedings of the 11th International Conference on Information Fusion, FUSION 2008

Other

Other11th International Conference on Information Fusion, FUSION 2008
CountryGermany
CityCologne
Period08/6/3008/7/3

Keywords

  • Data assimilation
  • Nonlinear state space model
  • Parameter identification
  • Particle filter
  • Pressure regulator

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

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  • Cite this

    Ishigaki, T., & Higuchi, T. (2008). Parameter identification of a pressure regulator with a nonlinear structure using a particle filter based on the nonlinear state space model. In Proceedings of the 11th International Conference on Information Fusion, FUSION 2008 [4632304] (Proceedings of the 11th International Conference on Information Fusion, FUSION 2008). https://doi.org/10.1109/ICIF.2008.4632304