An LFT approach to parameter estimation

Kenneth Hsu, Tyrone Vincent, Greg Wolodkin, Sundeep Rangan, Kameshwar Poolla

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we consider a unified framework for parameter estimation problems. Under this framework, the unknown parameters appear in a linear fractional transformation (LFT). A key advantage of the LFT problem formulation is that it allows us to efficiently compute gradients, Hessians, and Gauss-Newton directions for general parameter estimation problems without resorting to inefficient finite-difference approximations. The generality of this approach also allows us to consider issues such as identifiability, persistence of excitation, and convergence for a large class of model structures under a single unified framework.

Original languageEnglish (US)
Pages (from-to)3087-3092
Number of pages6
JournalAutomatica
Volume44
Issue number12
DOIs
StatePublished - Dec 2008

Keywords

  • Linear fractional transformation
  • Maximum likelihood
  • Parameter estimation
  • System identification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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