Parameter reduction of nonlinear least-squares estimates via nonconvex optimization

Ryozo Nagamune, Jongeun Choi

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

4 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 reparameterized by an affine function of the smaller number of uncorrelated parameters. The correlation among uncertain parameters is detected by optimization with a bilinear matrix inequality. A numerical example illustrates the usefulness of the proposed technique.

Original languageEnglish
Title of host publication2008 American Control Conference, ACC
Pages1298-1303
Number of pages6
DOIs
Publication statusPublished - 2008 Sep 30
Event2008 American Control Conference, ACC - Seattle, WA, United States
Duration: 2008 Jun 112008 Jun 13

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2008 American Control Conference, ACC
CountryUnited States
CitySeattle, WA
Period08/6/1108/6/13

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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  • Cite this

    Nagamune, R., & Choi, J. (2008). Parameter reduction of nonlinear least-squares estimates via nonconvex optimization. In 2008 American Control Conference, ACC (pp. 1298-1303). [4586672] (Proceedings of the American Control Conference). https://doi.org/10.1109/ACC.2008.4586672