Research Papers: Gas Turbines: Controls, Diagnostics, and Instrumentation

Optimal Tuner Selection for Kalman Filter-Based Aircraft Engine Performance Estimation

[+] Author and Article Information
Donald L. Simon, Sanjay Garg

 NASA Glenn Research Center, 21000 Brookpark Road, MS 77-1 Cleveland, OH 44135

J. Eng. Gas Turbines Power 132(3), 031601 (Dec 02, 2009) (10 pages) doi:10.1115/1.3157096 History: Received March 20, 2009; Revised April 27, 2009; Published December 02, 2009; Online December 02, 2009

A linear point design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multivariable iterative search routine that seeks to minimize the theoretical mean-squared estimation error. This paper derives theoretical Kalman filter estimation error bias and variance values at steady-state operating conditions, and presents the tuner selection routine applied to minimize these values. Results from the application of the technique to an aircraft engine simulation are presented and compared with the conventional approach of tuner selection. Experimental simulation results are found to be in agreement with theoretical predictions. The new methodology is shown to yield a significant improvement in on-line engine performance estimation accuracy.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 2

T40 estimation (tuner comparison)

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Figure 3

T50 estimation (tuner comparison)

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Figure 4

Fn estimation (tuner comparison)

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Figure 5

SmLPC estimation (tuner comparison)

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Figure 6

Illustration of tuner impact on estimator response

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Figure 1

Flowchart of V∗ iterative optimal search



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