Parameter reduction of nonlinear least-squares estimates via the singular value decomposition

Ryozo Nagamune, Jongeun Choi

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

3 Citations (Scopus)

Abstract

This paper proposes a technique for reducing the number of uncertain parameters in order to simplify robust and adaptive controller design. The system is assumed to have a known structure with parametric uncertainties that represent plant dynamics variation. An original set of parameters is identified by nonlinear least-squares (NLS) optimization using noisy frequency response functions. Using the property of asymptotic normality for NLS estimates, the original parameter set is re-parameterized by an affine function of the smaller number of uncorrelated parameters. The correlation among uncertain parameters over NLS estimates from different plants is detected by the singular value decomposition. A numerical example illustrates the usefulness of the proposed techniques.

Original languageEnglish
Title of host publicationProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Edition1 PART 1
DOIs
Publication statusPublished - 2008
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: 2008 Jul 62008 Jul 11

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1 PART 1
Volume17
ISSN (Print)1474-6670

Other

Other17th World Congress, International Federation of Automatic Control, IFAC
CountryKorea, Republic of
CitySeoul
Period08/7/608/7/11

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

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