Closed-loop identification: Application to the estimation of limb impedance in a compliant environment

David T. Westwick, Eric J. Perreault

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

The force and position data used to construct models of limb impedance are often obtained from closed-loop experiments. If the system is tested in a stiff environment, it is possible to treat the data as if they were obtained in open loop. However, when limb impedance is studied in a compliant environment, the presence of feedback cannot be ignored. While unbiased estimates of a system can be obtained directly using the prediction error method, the same cannot be said when linear regression or correlation analysis is used to fit nonparametric time- or frequency-domain models. We develop a prediction error minimization-based identification method for a nonparametric time-domain model augmented with a parametric noise model. The identification algorithm is tested on a dynamic mass-spring-damper system and returns consistent estimates of the system's properties under both stiff and compliant feedback control. The algorithm is then used to estimate the impedance of a human elbow joint in both stiff and compliant environments.

Original languageEnglish (US)
Pages (from-to)521-530
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume58
Issue number3 PART 1
DOIs
StatePublished - Mar 1 2011

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Keywords

  • Autoregressive moving average (ARMA)
  • joint dynamics
  • limb impedance
  • noise model
  • separable least squares
  • system identification

ASJC Scopus subject areas

  • Biomedical Engineering

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