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

Abstract An enhanced design methodology for minimizing the error in online Kalman filter- -based aircraft engine performance estimation applications is presented in this paper. It specific-ally addresses the underdetermined 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 para-meter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. While the technique was existing 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. he Ttheoretical Kalman filter mean squared estimation error at a steady-state closed-loop operating is derivedpoint, and the tuner selection applied to minimize approach this error is discussed. A technique for constructing a globally optimal tuning parameter vector, which enables full-envelope applica-tion 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 signifi-cant improvement in on-line Kalman filter estimation accuracy.