Model-based techniques for virtual sensing of longitudinal flight parameters

Hardier, G. ; Seren, C. ; Ezerzere, P.

International Journal of Applied Mathematics and Computer Science, Tome 25 (2015) no. 1, pp. 23-38.

Résumé

Introduction of fly-by-wire and increasing levels of automation significantly improve the safety of civil aircraft, and result in advanced capabilities for detecting, protecting and optimizing A/C guidance and control. However, this higher complexity requires the availability of some key flight parameters to be extended. Hence, the monitoring and consolidation of those signals is a significant issue, usually achieved via many functionally redundant sensors to extend the way those parameters are measured. This solution penalizes the overall system performance in terms of weight, maintenance, and so on. Other alternatives rely on signal processing or model-based techniques that make a global use of all or part of the sensor data available, supplemented by a model-based simulation of the flight mechanics. That processing achieves real-time estimates of the critical parameters and yields dissimilar signals. Filtered and consolidated information is delivered in unfaulty conditions by estimating an extended state vector, including wind components, and can replace failed signals in degraded conditions. Accordingly, this paper describes two model-based approaches allowing the longitudinal flight parameters of a civil A/C to be estimated on-line. Results are displayed to evaluate the performances in different simulated and real flight conditions, including realistic external disturbances and modeling errors.

Détail

Keywords: model based estimation, fault detection, virtual sensor, Kalman filtering, surrogate modelling
Mots-clés : estymacja modelu, detekcja uszkodzeń, sensor wirtualny, filtracja Kalmana

Bibliographie

[1] Arulampalam, S., Maskell, S. and Gordon, N. (2002). A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing 50(2): 174–188.

[2] Boiffier, J.-L. (1998). The Dynamics of Flight: The Equations, John Wiley Sons, Chichester.

[3] Bucharles, A., Cumer, C., Hardier, G., Jacquier, B., Janot, A., Le Moing, T., Seren, C., Toussaint, C. and Vacher, P. (2012). An overview of relevant issues for aircraft model identification, ONERA, AerospaceLab Journal (4): 13–33.

[4] Chang, C.T. and Chen, J.W. (1995). Implementation issues concerning the EKF-based fault diagnosis techniques, Chemical Engineering Science 50(18): 2861–2882.

[5] Chen, R. and Liu, J.S. (2000). Mixture Kalman filters, Journal of the Royal Statistical Society 62(3): 493–508.

[6] Chen, S., Hong, X., Harris, C.J. and Sharkey, P.M. (2004). Sparse modelling using orthogonal forward regression with PRESS statistic and regularization, IEEE Transactions on Systems, Man and Cybernetics 34(2): 898–911.

[7] Chen, S., Hong, X., Luk, B.L. and Harris, C.J. (2009). Non-linear system identification using particle swarm optimization tuned radial basis function models, International Journal of Bio-Inspired Computation 1(4): 246–258.

[8] Clerc, M. (2006). Particle Swarm Optimization, ISTE, London.

[9] Davies, M. (2003). The Standard Handbook for Aeronautical and Astronautical Engineers, McGraw-Hill, New York, NY.

[10] De Freitas, N. (2002). Rao–Blackwellised particle filtering for fault diagnosis, Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA, pp. 1767–1772.

[11] Dennis, J.E. and Schnabel, R.B. (1996). Numerical Methods for Unconstrained Optimization and Nonlinear Equations, SIAM, Philadelphia, PA.

[12] Frank, P.M. (1996). Analytical and qualitative model-based fault diagnosis: A survey and some new results, European Journal of Control 2(1): 6–28.

[13] Garcia, E.A. and Frank, P.M. (1997). Deterministic nonlinear observer-based approaches to fault diagnoses: A survey, Control Engineering Practice 5(5): 663–670.

[14] Ghanbarpour Asl, H. and Pourtakdoust, S.H. (2007). UD covariance factorization for unscented Kalman filter using sequential measurements update, World Academy of Science, Engineering and Technology (34): 368–376.

[15] Goupil, P. (2010). Oscillatory failure case detection in the A380 electrical flight control system by analytical redundancy, Control Engineering Practice 18(9): 1110–1119.

[16] Goupil, P. (2011). Airbus state of the art and practices on FDI and FTC in flight control system, Control Engineering Practice 19(6): 524–539.

[17] Hanlon, P.D. and Maybeck, P.S. (2000). Multiple-model adaptive estimation using a residual correlation Kalman filter bank, IEEE Transactions on Aerospace and Electronic Systems 36(2): 393–406.

[18] Hardier, G. (1998). Recurrent RBF networks for suspension system modeling and wear diagnosis of a damper, Proceedings of the IEEE World Congress on Computational Intelligence, Anchorage, AK, USA, Vol. 3, pp. 2441–2446.

[19] Hardier, G., Roos, C. and Seren, C. (2013). Creating sparse rational approximations for linear fractional representations using surrogate modeling, Proceedings of the 3rd International Conference on Intelligent Control and Automation Science, Chengdu, China, pp. 238–243.

[20] Hardier, G. and Seren, C. (2013). Aerodynamic model inversion for virtual sensing of longitudinal flight parameters, Proceedings of the 2nd International Conference on Control and Fault Tolerant Systems, Nice, France, pp. 140–145.

[21] Isermann, R. (2008). Model-based fault-detection and diagnosis: Status and applications, Annual Reviews in Control 29(1): 71–85.

[22] Jategaonkar, R.V. (2006). Flight Vehicle System Identification: A Time Domain Methodology, F.K. Lu Edition, AIAA, Inc., Arlington, VA.

[23] Julier, S.J. and Uhlmann, J.K. (2004). Unscented filtering and nonlinear estimation, Proceedings of the IEEE 92(3): 401–422.

[24] Kavuri, S.N., Venkatasubramanian, V., Rengaswamy, R. and Yin, K. (2003). A review of process fault detection and diagnosis, Computers and Chemical Engineering 27(3): 293–346.

[25] Kay, S.M. and Marple, S.L. (1981). Spectrum analysis—A modern perspective, Proceedings of the IEEE69(11): 1380–1419.

[26] Lu, S., Cai, L., Ding, L. and Chen, J. (2007). Two efficient implementation forms of unscented Kalman filter, Proceedings of the IEEE International Conference on Control and Automation, Guangzhou, China, pp. 761–764.

[27] Marzat, J., Piet-Lahanier, H., Damongeot, F. and Walter, E. (2012). Model-based fault diagnosis for aerospace systems: A survey, Journal of Aerospace Engineering 226(10): 1329–1360.

[28] Morelli, E.A. and DeLoach, R. (2003). Wind tunnel database development using modern experiment design and multivariate orthogonal functions, Proceedings of the 41st AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, USA, AIAA 2003–653.

[29] Nelles, O. and Isermann, R. (1996). Basis function networks for interpolation of local linear models, Proceedings of the 35th IEEE International Conference on Decision and Control, Kobe, Japan, pp. 470–475.

[30] Oosterom, M. and Babuska, R. (2000). Virtual sensor for FDI in flight control systems—fuzzy modeling approach, Proceedings of the 39th IEEE Conference on Decision and Control, Sydney, Australia, pp. 2645–2650.

[31] Patton, R.J. and Chen, J. (1994). A review of parity space approaches to fault diagnosis for aerospace systems, Journal of Guidance, Control and Dynamics 17(2): 278–285.

[32] Ru, J. and Li, R. (2003). Interacting multiple model algorithm with maximum likelihood estimation for FDI, Proceedings of the IEEE International Symposium on Intelligent control, Houston, TX, USA, pp. 661–666.

[33] Samara, P.A., Fouskitakis, G.N., Sakellariou, J.S. and Fassois, S.D. (2008). A statistical method for the detection of sensor abrupt faults in aircraft control systems, IEEE Transactions on Control Systems Technology 16(4): 789–798.

[34] Seren, C. and Hardier, G. (2013). Adaptive extended Kalman filtering for virtual sensing of longitudinal flight parameters, Proceedings of the 2nd International Conference on Control and Fault Tolerant Systems, Nice, France, pp. 25–30.

[35] Seren, C., Hardier, G. and Ezerzere, P. (2011). On-line estimation of longitudinal flight parameters, Proceedings of the SAE AeroTech Congress and Exhibition, Toulouse, France.

[36] Smidl, V. and Peroutka, Z. (2012). Advantages of square-root extended Kalman filter for sensorless control of AC drives, IEEE Transactions on Industrial Electronics 59(11): 4189–4196.

[37] Traverse, P., Lacaze, I. and Souryis, J. (2004). Airbus fly-by-wire: A total approach to dependability, Proceedings of the 18th IFIP World Computer Congress, Toulouse, France, pp. 191–212.

[38] Van der Merwe, R. and Wan, E. (2001). Efficient derivative free Kalman filters, Proceedings of the 9th European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 205–210.

[39] Zolghadri, A. (2012). Advanced model-based FDIR techniques for aerospace systems: Today challenges and opportunities, Progress in Aerospace Sciences 53: 18–29.