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Title: Nonlinear recursive estimation with estimability analysis for physical and semiphysical engine model parameters
Authors: Souflas, Ioannis
Pezouvanis, Antonios
Ebrahimi, Kambiz Morteza
Issue Date: 2016
Publisher: © ASME
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.
Abstract: 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.
Description: This paper is in closed access.
Version: Accepted for publication
DOI: 10.1115/1.4032052
URI: https://dspace.lboro.ac.uk/2134/20673
Publisher Link: http://dx.doi.org/10.1115/1.4032052
ISSN: 0022-0434
Appears in Collections:Closed Access (Aeronautical and Automotive Engineering)

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