System identification plays an important role in advanced control systems for jet engines, in which controls are performed adaptively using data from the actual engine and the identified engine. An identification technique for jet engine using the Constant Gain Extended Kalman Filter (CGEKF) is described. The filter is constructed for a two-spool turbofan engine. The CGEKF filter developed here can recognize parameter change in engine components and estimate unmeasurable variables over whole flight conditions. These capabilities are useful for an advanced Full Authority Digital Electric Control (FADEC). Effects of measurement noise and bias, effects of operating point and unpredicted performance change are discussed. Some experimental results using the actual engine are shown to evaluate the effectiveness of CGEKF filter. [S0742-4795(00)00401-4]

Issue Section:
Gas Turbines: Controls and Diagnostics
Keywords:
Kalman filters, aerospace engines, jets, performance evaluation, safety, testing, simulation, direct digital control, multivariable control systems
Topics:
Engines, Jet engines, Kalman filters, Noise (Sound), Simulation
1.
Kerr, L. J., Nemec, T. S., and Gallops, G. W., 1991, “Real-Time Estimation of Gas Turbine Engine Damage Using a Control Based Kalman Filter Algorithm,” ASME Paper 91-GT-216.
2.
Urban, L. A., and Volponi, A. J., 1992, “Mathematical Methods of Relative Engine Performance Diagnostics,” SAE 922048.
3.
Doel, D. L., 1992, “TEMPER—A Gas-Path Analysis Tool for Commercial Jet Engines,” ASME Paper 92-GT-315.
4.
Luppold, R. H., Roman, J. R., Gallops, G. W., and Kerr, L. J., 1989, “Estimating In-Flight Engine Performance Variations Using Kalman Filter Concepts,” AIAA 89-2584.
5.
Espana, M. D., 1993, “On the Estimation Algorithm for Adaptive Performance Optimization of Turbofan Engines,” AIAA 93-1823.
6.
Adibhatla, S., and Johnson, K. L., 1993, “Evaluation of a Nonlinear PSC Algorithm on a Variable Cycle Engine,” AIAA 93-2077.
7.
Orme, J. S., and Conners, T. R., 1994, “Supersonic Flight Test Results of a Performance Seeking Control Algorithm on a NASA F-15 Aircraft,” AIAA 94-3210.
8.
Merrill
,
W. C.
,
DeLaat
,
J. C.
, and
Bruton
,
W. M.
,
1988
, “
Advanced Detection, Isolation, and Accommodation of Sensor Failures—Real-Time Evaluation
,”
J. Guidance
,
11
, No.
6
, pp.
517
–
526
.
9.
Volponi, A. J., 1994, “Sensor Error Compensation in Engine Performance Diagnostics,” ASME Paper 94-GT-58.
10.
Safonov
,
M. G.
, and
Athans
,
M.
,
1978
, “
Robustness and Computational Aspects of Nonlinear Stochastic Estimators and Regulators
,”
IEEE Trans. Autom. Control.
,
AC-23
, No.
4
, pp.
717
–
725
.
11.
Misawa
,
E. A.
, and
Hedrick
,
J. K.
,
1989
, “
Nonlinear Observers—A State-of-the-Art Survey
,”
ASME J. Dyn. Syst., Meas., Control
,
111
, pp.
344
–
352
.
12.
Sugiyama, N., 1990, “Generalized High Speed Simulation of Gas Turbine Engines,” ASME Paper 90-GT-270.
13.
Seldner, K., Mihaloew, J. R., and Blaha, R. J., 1972, “Generalized Simulation Technique for Turbojet Engine System Analysis,” NASA TN D-6610.
14.
Sugiyama
,
N.
,
1994
, “
Derivation of System Matrices From Nonlinear Dynamic Simulation of Jet Engines
,”
J. Guid. Control. Dyn.
,
17
, No.
6
, pp.
1320
–
1326
.
15.
Adibhatla, S., and Gastineau, Z., 1994, “Tracking Filter Selection and Control Mode Selection for Model Based Control,” AIAA 94-3204.
16.
Sugiyama, N., 1996, “On-Line System Identification of Jet Engine,” AIAA 96-2590.
Copyright © 2000
 by ASME
You do not currently have access to this content.