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Nonlinear recursive estimation with estimability analysis for physical and semiphysical engine model parameters
journal contributionposted on 22.03.2016, 13:25 authored 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.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering