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Nonlinear recursive estimation with estimability analysis for physical and semiphysical engine model parameters
journal contribution
posted on 2016-03-22, 13:25 authored by Ioannis Souflas, Antonios PezouvanisAntonios Pezouvanis, Kambiz EbrahimiKambiz EbrahimiA 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 ControlVolume
138Issue
2Pages
? - ? (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
© ASMEVersion
- 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
2015-12-11Notes
This paper is in closed access.ISSN
0022-0434Publisher version
Language
- en