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Flight Parameter Estimation for Augmented Flight Control System Autonomy
ISSN: 0148-7191, e-ISSN: 2688-3627
Published October 18, 2011 by SAE International in United States
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In the framework of the aircraft global optimization, for future and upcoming programs, current research interests include more Electrical Flight Control System (EFCS) autonomy for a more easy-to-handle aircraft. A possible solution is to increase the number of redundant flight parameter sensors but to the detriment of the aircraft weight and so to the cost and performances. This paper proposes an algorithm using PLS (Partial Least Squares) to estimate a flight parameter from independent sensor measurements. The estimates are then used as so-called “software” or “virtual” sensors, allowing aircraft weight saving. This algorithm is based on an iterative processing and thus can be used in real time in the embedded flight control computer. Furthermore, the resulting flight parameter estimates can be used to detect failures. Different detection strategies are proposed and results show that this method can lead to robust detections.
CitationCazes, F., Mailhes, C., Chabert, M., Goupil, P. et al., "Flight Parameter Estimation for Augmented Flight Control System Autonomy," SAE Technical Paper 2011-01-2801, 2011, https://doi.org/10.4271/2011-01-2801.
- Traverse, P., Lacaze, I. and Souyris, J. (2004). Airbus Fly-By-Wire: A Total Approach To Dependability. Proc. 18th IFIP World Computer Congress, Toulouse, France, pp. 191-212.
- Favre, C. (1994). Fly-by-wire for commercial aircraft: the Airbus experience. International Journal of Control, 59(1), 139-157.
- Goupil, P. (2011). AIRBUS State of the Art and Practices on FDI and FTC in Flight Control System. Control Engineering Practice 19(2011), pp. 524-539, doi:10.1016/j.conengprac.2010.12.009.
- Rosenberg, K. (1998) “FCS architecture definition (issue 1)” Deliverable 3.4, BE97-4098 ADFCS.
- Patton, R.J., Frank, P.M., and Clark, R.N. (1989), Fault Diagnosis in Dynamic Systems, Theory and Applications. New York: Prentice-Hall, 1989.
- Chen, J. and Patton, R.J. (1999). Robust model-based fault diagnosis for dynamic systems. Kluwer Academic Publishers.
- Ding, S.X. (2008). Model-based Fault Diagnosis Techniques. Design Schemes, Algorithms, and Tools. Springer, Heidelberg, Berlin.
- Zolghadri, A. (2002). Early warning and prediction of flight parameter abnormalities for improved system safety assessment. Reliability Engineering and System Safety, vol. 16, pages 19-27.
- Goupil, P. (2010). Oscillatory Failure Case Detection In The A380 Electrical Flight Control System By Analytical Redundancy, Control Engineering Practice 18 (2010), pp. 1110-1119. DOI information: 10.1016/j.conengprac.2009'04.003.
- Tenenhaus, Michel (1998). La regression PLS, théorie et pratique, [ed.] Technip. P. 254. ISBN-10: 2710807351.
- Chiang, L.H., Russell, E.L., Braatz, R.D. (2001). Fault Detection and Diagnosis in Industrial Systems. S.1.: Springer. P. 279. ISBN 1-85233-327-8.
- Haykin, Simon (2003). Least-Mean-Square Adaptative Filters (Adaptive and Learning Systems for Signal Processing, Communications and Control Series). S.L: Wiley-Interscience. P. 512. 0471215708.