Robust Passive Fault Tolerant Control Applied to Jet Engine Equipment
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
Fault tolerant control, Model Identification, Linear Parameter Variable (LPV), Robustness, Multi-Model, Neural Extended Kalman Filter (NEKF), Virtual sensor, Takagi-sugeno, robuste estimation
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.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.