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Optimal input design for minimum-variance estimation of parameters in nonlinear state-space models

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Abstract

The paper presents a methodology for optimal input design (OID) for minimum-variance estimation of parameters in nonlinear state-space models. To allow analytical solutions, in Keesman (2015) asequential OID approach, based on Pontryagin's minimum principle, was proposed for low-dimensional non-linear systems that are affine in their input. In this study, a simultaneous OID approach for a set of two parameters in one-dimensional non-linear systems affine in their input is presented. For the two-parameter case still analytically tractable solutions are found, unlike cases with three or more parameters.

Original languageEnglish
Title of host publication18th IFAC Symposium on System Identification SYSID 2018
EditorsC. Rojas
PublisherIFAC
Pages365-370
DOIs
Publication statusPublished - 8 Oct 2018

Publication series

NameIFAC-PapersOnLine
PublisherElsevier
Number15
Volume51
ISSN (Print)2405-8963

Keywords

  • Non-linear dynamic systems
  • Optimal Input Design
  • Parameter estimation
  • Pontryagin's principle
  • Two-parameter case

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