An Onboard Aeroengine Model-Tuning System
Publication: Journal of Aerospace Engineering
Volume 30, Issue 4
Abstract
This study investigates an onboard aeroengine model (OBEM)-tuning system (OTS) based on a hybrid Kalman filter, which is mostly used to estimate the aeroengine performance. The OTS structure comprises two parts. One is an OBEM, the other a linear parameter-varying Kalman filter. The Kalman filter is used as a regulator to minimize the mismatch of the measured outputs between the OBEM and the actual engine. This study describes the method and procedure used to construct the OTS. The results from the application of the technique to the aeroengine simulations are presented and compared to the conventional approach of tuner selection. The new methodology is shown to yield a significant improvement in online Kalman filter estimation accuracy.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant 61573035 and the China Scholarship Council under Grant 201506025135.
References
Armstrong, J. B., and Simon, D. L. (2011). “Implementation of an integrated on-board aircraft engine diagnostic architecture.” Proc., 47th AIAA/ASME/SAE/ASEE Joint Propulsion Conf. and Exhibit, American Institute of Aeronautics and Astronautics, Reston, VA.
Armstrong, J. B., and Simon, D. L. (2012). “Constructing an efficient self-tuning aircraft engine model for control and health management applications.”, NASA, Cleveland.
Bavdekar, V. A., Deshpande, A. P., and Patwardhan, S. C. (2011). “Identification of process and measurement noise covariance for state and parameter estimation using extended Kalman filter.” J. Process Control, 21(4), 585–601.
Behbahani, A., Adibhatla, S., and Rauche, C. (2009). “Integrated model-based controls and phm for improving turbine engine performance, reliability, and cost.” Proc., 45th AIAA/ASME/SAE/ASEE Joint Propulsion Conf. and Exhibit, American Institute of Aeronautics and Astronautics, Reston, VA.
Bemporad, A., Morari, M., Dua, V., and Pistikopoulos, E. N. (2002). “The explicit linear quadratic regulator for constrained systems.” Automatica, 38(1), 3–20.
Brotherton, T., Volponi, A., Luppold, R., and Simon, D. L. (2003). “eSTORM: Enhanced self tuning on-board real-time engine model.” Intelligent Automation Corp., Poway, CA.
Chen, C. (2015). “On the robustness of the linear quadratic regulator via perturbation analysis of the Riccati equation.” Ph.D. thesis, Dublin City Univ., Dublin, Ireland.
Dukeman, G. (2002). “Profile-following entry guidance using linear quadratic regulator theory.” Proc., AIAA Guidance, Navigation, and Control Conf. and Exhibit, American Institute of Aeronautics and Astronautics, Reston, VA.
Garg, S. (2004). “Controls and health management technologies for intelligent aerospace propulsion systems.” Proc., 42nd AIAA Aerospace Sciences Meeting and Exhibit, American Institute of Aeronautics and Astronautics, Reston, VA.
Garg, S. (2007). “Propulsion controls and diagnostics research at nasa glenn.” Proc., 43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conf. and Exhibit, American Institute of Aeronautics and Astronautics, Reston, VA.
Grindle, T. J., and Burcham, F. W. J. (2003). “Engine damage to a nasa dc-8-72 airplane from a high-altitude encounter with a diffuse volcanic ash cloud.”, NASA, Cleveland.
Jaw, L. C., and Mattingly, J. D. (2009). Aircraft engine controls—Design, system analysis, and health monitoring, American Institute of Aeronautics and Astronautics, Reston, VA.
Kobayashi, T., and Simon, D. L. (2006). “Hybrid Kalman filter: A new approach for aircraft engine in-flight diagnostics.”, ASRC Aerospace Corp. and Army Research Lab, Cleveland.
Kobayashi, T., and Simon, D. L. (2007a). “Hybrid Kalman filter approach for aircraft engine in-flight diagnostics: Sensor fault detection case.” J. Eng. Gas. Turbines Power, 129(3), 746–754.
Kobayashi, T., and Simon, D. L. (2007b). “Integration of on-line and off-line diagnostic algorithms for aircraft engine health management.” J. Eng. Gas. Turbines Power, 129(4), 986–993.
Litt, J. S., et al. (2004). “A survey of intelligent control and health management technologies for aircraft propulsion systems.” J. Aerosp. Comput. Inf. Commun., 1(12), 543–563.
Liu, X. F., and An, S. Q. (2016). “Smooth switching controller design for multiobjective control systems and applications.” J. Aerosp. Eng., .
Pham, D. T., Verron, J., and Roubaud, M. C. (1998). “A singular evolutive extended Kalman filter for data assimilation in oceanography.” J. Mar. Syst., 16(3), 323–340.
Pourbabaee, B., Meskin, N., and Khorasani, K. (2013). “Multiple-model based sensor fault diagnosis using hybrid Kalman filter approach for nonlinear gas turbine engines.” Proc., American Control Conf., IEEE, New York, 4717–4723.
Reberga, L., Henrion, D., Bernussou, J., and Vary, F. (2005). “LPV modeling of a turbofan engine.” Proc., 16th IFAC World Congress, Elsevier, Amsterdam, Netherlands.
Rinehart, A. W., and Simon, D. L. (2014). “An integrated architecture for aircraft engine performance monitoring and fault diagnostics: Engine test results.” Proc., 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conf., American Institute of Aeronautics and Astronautics, Reston, VA.
Schlee, F., Standish, C., and Toda, N. (1967). “Divergence in the Kalman filter.” AIAA J., 5(6), 1114–1120.
Simon, D. L., and Armstrong, J. B. (2013). “An integrated approach for aircraft engine performance estimation and fault diagnostics.” J. Eng. Gas. Turbines Power, 135(7), 071203.
Simon, D. L., and Garg, S. (2009). “A systematic approach for model-based aircraft engine performance estimation.” Proc., AIAA Infotech at Aerospace Conf., American Institute of Aeronautics and Astronautics, Reston, VA.
Tóth, R. (2010). Modeling and identification of linear parameter-varying systems, Vol. 403, Springer, New York.
Turso, J. A., and Litt, J. S. (2005). “A foreign object damage event detector data fusion system for turbofan engines.” J. Aerosp. Comput. Inf. Commun., 2(7), 291–308.
Volponi, A. (2008). “Enhanced self tuning on-board real-time model (eSTORM) for aircraft engine performance health tracking.”, NASA, Cleveland.
Volponi, A., Brotherton, T., and Luppold, R. (2008). “Empirical tuning of an on-board gas turbine engine model for real-time module performance estimation.” J. Eng. Gas. Turbines Power, 130(2), 021604.
Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 30 • Issue 4 • July 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Dec 15, 2015
Accepted: Nov 23, 2016
Published online: Feb 13, 2017
Published in print: Jul 1, 2017
Discussion open until: Jul 13, 2017
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