File(s) under permanent embargo

Reason: This item is currently closed access.

Nonlinear recursive estimation with estimability analysis for physical and semiphysical engine model parameters

journal contribution
posted on 22.03.2016, 13:25 by Ioannis Souflas, Antonios PezouvanisAntonios Pezouvanis, Kambiz EbrahimiKambiz Ebrahimi
A methodology for nonlinear recursive parameter estimation with parameter estimability analysis for physical and semi-physical engine models is presented. Orthogonal estimability analysis based on parameter sensitivity is employed with the purpose of evaluating a rank of estimable parameters given multiple sets of observation data that were acquired from a transient engine testing facility. The qualitative information gained from the estimability analysis is then used for estimating the estimable parameters by using two well-known nonlinear adaptive estimation algorithms known as Extended and Unscented Kalman Filters. The findings of this work contribute on understanding the real-world challenges which are involved in the effective implementation of system identification techniques suitable for online nonlinear estimation of parameters with physical interpretation.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

Journal of Dynamic Systems Measurement and Control

Volume

138

Issue

2

Pages

? - ? (5)

Citation

SOUFLAS, I., PEZOUVANIS, A. and EBRAHIMI, K.M., 2016. Nonlinear recursive estimation with estimability analysis for physical and semiphysical engine model parameters. Journal of Dynamic Systems Measurement and Control, 138(2), 024502.

Publisher

© ASME

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2015-12-11

Notes

This paper is in closed access.

ISSN

0022-0434

Language

en

Usage metrics

Keywords

Exports