Parameter identification of a suspension system and road disturbance estimation

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

An identification method is presented for the parameters of a quasi Linear Parameter Varying (qLPV) model of a quarter car suspension system where non-linear components are taken into consideration. Because not all the variables necessary for the identification are measured, some numerical techniques need to be applied, e.g., the numerical integration of measured signals. It is also shown that the selection of the sampling time might be critical in this type of application. By using the result of this identification an algorithm is given for the road disturbance reconstruction. The road roughness estimation is based on this reconstructed signal.

Original languageEnglish
Pages (from-to)128-137
Number of pages10
JournalInternational Journal of Vehicle Systems Modelling and Testing
Volume2
Issue number2
DOIs
Publication statusPublished - 2007

Fingerprint

Parameter Identification
Identification (control systems)
Railroad cars
Disturbance
Surface roughness
Sampling
Numerical Techniques
Roughness
Numerical integration
Necessary
Model

Keywords

  • Linear parameter varying control
  • LPV
  • Non-linear modelling
  • Road vehicle
  • Robust control
  • Uncertainty structure

ASJC Scopus subject areas

  • Automotive Engineering
  • Modelling and Simulation

Cite this

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abstract = "An identification method is presented for the parameters of a quasi Linear Parameter Varying (qLPV) model of a quarter car suspension system where non-linear components are taken into consideration. Because not all the variables necessary for the identification are measured, some numerical techniques need to be applied, e.g., the numerical integration of measured signals. It is also shown that the selection of the sampling time might be critical in this type of application. By using the result of this identification an algorithm is given for the road disturbance reconstruction. The road roughness estimation is based on this reconstructed signal.",
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AU - Szabó, Z.

AU - Bokor, J.

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