Robust Passive Fault Tolerant Control Applied to Jet Engine Equipment



Published Jul 8, 2014


In order to minimize the occurrence of unexpected costly flight failures modern aircraft engines industry focuses especially on increasing product’s availability. In this work, we propose to monitor the health of a Variable Stator Vane (VSV), subsystem controlling the amount of airflow through the High Pressure Compressor (HPC), allowing optimum compressor performance. This control of airflow prevents the engine from stalling. The proposed methodology is based on an original approach for real time on-board Passive Fault Tolerant Control (PFTC). The objective of the proposed PFTC is to provide acceptable performance and preserve stability when faults occur. The method relies on the design of a specific Robust Virtual Sensor in a Linear Parameter Variable (LPV) polytopic framework. The robustness to model uncertainties is ensured by a Neural Extended Kalman Filter (NEKF) accommodating, in real time, the model prediction. In the proposed methodology, an off-line closed-loop identification scheme is first used to elaborate a multi local linear state space models, after that a multi-model observer based on Linear Matrix Inequalities
(LMI) optimization is used to build the virtual sensor. The NEKF is added to circumvent online model accuracy problems. The efficiency and limit of the approach are shown and discussed through simulations on a complete numerical engine test bench.

How to Cite

SOUAMI, Y., MECHBAL, N., & ECOUTIN, S. (2014). Robust Passive Fault Tolerant Control Applied to Jet Engine Equipment. PHM Society European Conference, 2(1).
Abstract 1445 | PDF Downloads 106



Fault tolerant control, Model Identification, Linear Parameter Variable (LPV), Robustness, Multi-Model, Neural Extended Kalman Filter (NEKF), Virtual sensor, Takagi-sugeno, robuste estimation

Akhenak, A., Chadli, M., Ragot, J. and Maquin, D., 2007. Design of sliding mode unknown input observer for uncertain Takagi-Sugeno model, Control & Automation, 2007. MED'07. Mediterranean Conference on 2007, IEEE, pp. 1-6.
Bezzaoucha, S., Marx, B., Maquin, D. and Ragot, J., State and Parameter Estimation for Time-varying Systems: a Takagi-Sugeno Approach, .
Bezzaoucha, S., Marx, B., Maquin, D. and Ragot, J., 2013. Nonlinear joint state and parameter estimation: Application to a wastewater treatment plant. Control Engineering Practice, 21(10), pp. 1377-1385.
Bezzaoucha, S., Marx, B., Maquin, D. and Ragot, J., 2013. Stabilization of nonlinear systems subject to actuator saturation, Fuzzy Systems (FUZZ), 2013 IEEE International Conference on 2013, pp. 1-6.
Blanke, M., Staroswiecki, M. and Wu, N.E., 2001. Concepts and methods in fault-tolerant control, American Control Conference, 2001. Proceedings of the 2001 2001, IEEE, pp. 2606-2620.
De oca, S.M. and Puig, V., 2010. Fault-tolerant control design using a virtual sensor for lpv systems, Control and Fault-Tolerant Systems (SysTol), 2010 Conference on 2010, IEEE, pp. 88-93.
De oca, S., Puig, V, Witczak, M. and Dziekan, Ł, 2012. Fault-tolerant control strategy for actuator faults using LPV techniques: Application to a two degree of freedom helicopter. International Journal of Applied Mathematics and Computer Science, 22(1), pp. 161-171.
Kramer, K.A. and Stubberud, S.C., 2008. Tracking of multiple target types with a single neural extended Kalman filter. Intelligent Techniques and Tools for Novel System Architectures. Springer, pp. 495-512.
Laprie, J., 1985. Dependable computing and fault-tolerance. Digest of Papers FTCS-15, , pp. 2-11.
Liao, F., Wang, J.L. and Yang, G., 2002. Reliable robust flight tracking control: an LMI approach. Control Systems Technology, IEEE Transactions on, 10(1), pp. 76-89.
Nazari, R., Seron, M.M. and De doná, J.A., 2013. Faulttolerant control of systems with convex polytopic linear parameter varying model uncertainty using virtualsensor-based controller reconfiguration. Annual Reviews in Control, 37(1), pp. 146-153.
Owen, M.W. and Stubberud, A.R., 2003. A neural extended Kalman filter multiple model tracker, OCEANS 2003. Proceedings 2003, IEEE, pp. 2111-2119.
Patton, R.J., 1997. Fault-tolerant control systems: The 1997 situation, IFAC symposium on fault detection supervision and safety for technical processes 1997, pp. 1033-1054.
Richter, J.H., Heemels, W., Van de wouw, N. and Lunze, J., 2011. Reconfigurable control of piecewise affine systems with actuator and sensor faults: stability and tracking. Automatica, 47(4), pp. 678-691.
Stubberud, S.C., 2006. System Identification using the Neural-Extended Kalman Filter for Control Modification, Neural Networks, 2006. IJCNN'06. International Joint Conference on 2006, IEEE, pp.
Stubberud, S.C., Lobbia, R.N. and Owen, M., 1995. An adaptive extended Kalman filter using artificial neural networks, Decision and Control, 1995., Proceedings of the 34th IEEE Conference on 1995, pp. 1852-1856 vol.2.
Tafraouti, M., 2006. Contribution à la modélisation et la commande des systèmes électrohydrauliques. Université Henri Poincaré-Nancy I.
Yang, H., Jiang, B. and Cocquempot, V., 2010. Fault tolerant control design for hybrid systems, volume 397 de Lecture Notes in Control and Information Sciences.
Zhang, Y. and Jiang, J., 2008. Bibliographical review on reconfigurable fault-tolerant control systems. Annual reviews in control, 32(2), pp. 229-252.
Technical Papers

Similar Articles

1 2 3 4 5 6 7 8 9 10 > >> 

You may also start an advanced similarity search for this article.