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

Application of an Optimal Tuner Selection Approach for On-Board Self-Tuning Engine Models

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

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

Jeffrey B. Armstrong

 ASRC Aerospace Corporation, 21000 Brookpark Road, MS 500-ASRC, Cleveland, OH 44135

J. Eng. Gas Turbines Power 134(4), 041601 (Jan 27, 2012) (11 pages) doi:10.1115/1.4004178 History: Received April 25, 2011; Revised April 28, 2011; Published January 27, 2012; Online January 27, 2012

An enhanced design methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented in this paper. It specifically addresses the under-determined estimation problem, in which there are more unknown parameters than available sensor measurements. This work builds upon an existing technique for systematically selecting 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. While the existing technique was optimized for open-loop engine operation at a fixed design point, in this paper an alternative formulation is presented that enables the technique to be optimized for an engine operating under closed-loop control throughout the flight envelope. The theoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating point is derived, and the tuner selection approach applied to minimize this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope application of the technology, is also presented, along with design steps for adjusting the dynamic response of the Kalman filter state estimates. Results from the application of the technique to linear and nonlinear aircraft engine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in on-line Kalman filter estimation accuracy.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Aircraft engine, controller, and Kalman filter

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Figure 2

Optimization points applied for globally optimal tuner selection

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Figure 3

Kalman filter response with original Qxh

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Figure 4

Kalman filter response with adjusted Qxh

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