Recursive maximum likelihood parameter estimation for state space systems using polynomial chaos theory

Benjamin L. Pence, Hosam K. Fathy, Jeffrey L. Stein

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

This paper combines polynomial chaos theory with maximum likelihood estimation for a novel approach to recursive parameter estimation in state-space systems. A simulation study compares the proposed approach with the extended Kalman filter to estimate the value of an unknown damping coefficient of a nonlinear Van der Pol oscillator. The results of the simulation study suggest that the proposed polynomial chaos estimator gives comparable results to the filtering method but may be less sensitive to user-defined tuning parameters. Because this recursive estimator is applicable to linear and nonlinear dynamic systems, the authors portend that this novel formulation will be useful for a broad range of estimation problems.

Original languageEnglish (US)
Pages (from-to)2420-2424
Number of pages5
JournalAutomatica
Volume47
Issue number11
DOIs
StatePublished - Nov 2011

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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