Analysis of difficulty in estimating physically-meaningful model parameters based on normalized parameter sensitivity plot

Ichiro Maruta, Atsushi Baba, Shuichi Adachi

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

Abstract

In this paper, a new approach for analyzing the degree of the difficulty in estimating parameters is proposed. The approach targets at linear continuous-time models with physically-meaningful parameters and is based on the newly introduced normalized parameter sensitivity plot. The plot illustrates how the identification inputs contribute to the estimation accuracy in frequency domain and visualize the relative relationship between the model sensitivities for the parameters. In the paper, it is proven that there is a practically important accuracy limitation derived from the relative relationship. And the proposed plot enables to evaluate the limitation in a visual way. To show the applicability of the theoretical discussion in practical situations, a numerical experiment based on a mechanical system model is presented. And, a model of a rechargeable battery for electric vehicles is analyzed by the proposed approach to illustrate the effectiveness of the approach in devising strategies for estimating model parameters and selecting model structures.

Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Decision and Control
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6686-6691
Number of pages6
Volume2016-February
ISBN (Print)9781479978861
DOIs
Publication statusPublished - 2016 Feb 8
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: 2015 Dec 152015 Dec 18

Other

Other54th IEEE Conference on Decision and Control, CDC 2015
CountryJapan
CityOsaka
Period15/12/1515/12/18

Keywords

  • Analytical models
  • Estimation
  • Manganese
  • Mathematical model
  • Numerical models
  • Reactive power
  • Sensitivity

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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