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

Abstract

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 multi-variable iterative search routine which 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 to 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.

Aircraft Turbine Engine Control Research at NASA Glenn Research Center

“…Recently an innovative methodology (Simon and Garg 2010) has been developed at GRC that creates a tuning parameter vector defined as a linear combination of all health parameters and of appropriate dimension to enable Kalman filter estimation. Selection of this tuning parameter vector is performed using a multivariable iterative search routine that minimizes the theoretical mean-squared estimation error in the parameters of interest.…”

“…With the optimal tuner selection approach developed in (Simon and Garg 2010), sufficient estimation accuracy of the unmeasured variables can be obtained to allow for implementation of direct control of thrust and stall margins using the model estimated value. Research on this application is currently ongoing at GRC.…”

Implementation of an Integrated On-Board Aircraft Engine Diagnostic Architecture

“…While this will enable Kalman filterbased estimation, it can result in "smearing" the effects of un-estimated health parameters onto those that are estimated, and in turn introduce error in the accuracy of overall model-based performance estimation applications (Ref. 3).…”

A Risk Assessment Architecture for Enhanced Engine Operation

“…For instance, Ref. 16 describes an approach that has been demonstrated in simulation to produce an unbiased estimate of HPC stall margin with a standard deviation significantly less than the deterioration-induced debit. Incorporating any such estimation errors into the stack-up due to random variations (as in Eq.…”

“…For this example, the Engine Condition Monitoring block would contain algorithms that might estimate the level of deterioration of the engine, or even a measure of how close the engine's operating line is to stall. 15,16 Now, say that a family of acceleration schedules has been developed, appropriate for various levels of engine deterioration, and the most conservative schedule is the nominal schedule used in the controller. If the damaged flight control system determines that to damp the phugoid mode, for instance, the engine response time constant must be decreased to some particular value, the Control Mode Selector might request an evaluation of the risk of using a more aggressive acceleration schedule that would produce such a response, at the cost of a smaller stall margin transiently.…”