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Technical Brief

Nonlinear Recursive Estimation With Estimability Analysis for Physical and Semiphysical Engine Model Parameters

[+] Author and Article Information
Ioannis Souflas

Department of Aeronautical and Automotive Engineering,
Loughborough University,
Loughborough, Leicestershire LE11 3TU, UK
e-mail: i.souflas@lboro.ac.uk

Antonios Pezouvanis

Department of Aeronautical and Automotive Engineering,
Loughborough University,
Loughborough, Leicestershire LE11 3TU, UK
e-mail: a.pezouvanis@lboro.ac.uk

Kambiz M. Ebrahimi

Department of Aeronautical and Automotive Engineering,
Loughborough University,
Loughborough, Leicestershire LE11 3TU, UK
e-mail: k.ebrahimi@lboro.ac.uk

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 10, 2015; final manuscript received November 17, 2015; published online December 11, 2015. Assoc. Editor: Zongxuan Sun.

J. Dyn. Sys., Meas., Control 138(2), 024502 (Dec 11, 2015) (5 pages) Paper No: DS-15-1062; doi: 10.1115/1.4032052 History: Received February 10, 2015; Revised November 17, 2015

A methodology for nonlinear recursive parameter estimation with parameter estimability analysis for physical and semiphysical 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 Kalman filter (EKF) and unscented Kalman filter (UKF). 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.

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References

Figures

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Fig. 3

Test 2, ramp speed, and closed throttle

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Fig. 2

Test 1, constant speed, and closed throttle

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Fig. 1

Experimental apparatus

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Fig. 5

(a) Simulation and observation results and (b) zoom in −0.3 s

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Fig. 4

(a) Simulation and observation results and (b) zoom in −0.3 s

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