Fault Detection and Isolation for Autonomous Parafoils
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Abstract
Autonomous precision airdrop systems are widely used to de- liver supplies to remote locations. Payloads that are delivered far from their intended targets or with high impact velocity may be rendered unusable. Faults occurring during flight can severely degrade vehicle performance, effectively nullifying the value of the guided system, or worse. Quickly detecting and identifying faults enables the choice of an appropriate recovery strategy, potentially mitigating the consequences of an out-of-control vehicle and recovering performance. This paper presents a multi-observer, multi-residual fault detection and isolation (FDI) method for an autonomous parafoil system. The detection and isolation processes use residual signals generated from observers and other system models. statistical methods are applied to evaluate these residuals and determine whether a fault has occurred, given a priori knowledge of system uncertainty characteristics. Several examples are used to illustrate the detection and isolation algorithm on- line using available navigation and telemetry outputs. Tests of this FDI method on a large number of high-fidelity simulations indicate that it is possible to detect and isolate some common faults with a high percentage of success and a very small chance of raising a false alarm.
How to Cite
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Model-based, parafoil, FDI, observer-based, residual
Beard, R. V. (1971). Failure accommodation in linear systems through self-reorganization. Doctoral dissertation, Massachusetts Institute of Technology, Cam- bridge, MA.
Bergeron, K., Fejzic, A., & Tavan, S. (2011). Accuglide 100: Precision airdrop guidance and control via glide slope control. In 21th AIAA aerodynamic decelerator systems
technology conference and seminar. Dublin, Ireland.
Carter, D., George, S., Hattis, P. D., Singh, L., & Tavan, S. (2005). Autonomous guidance, navigation, and control of large parafoils. In 18th AIAA aerodynamic decelerator systems technology conference and seminar.Munich, Germany. doi: 10.2514/6.2005-1643
Crimi, P. (1990). Lateral stability of gliding parachutes. Journal of Guidance, Control, and Dynamics, 13(6), 1060-1063. doi: 10.2514/3.20579
Douglas, R. K., & Speyer, J. L. (1995). Robust fault detection filter design. In Proceedings of the American control conference (Vol. 1, p. 91-96). Seattle, WA. doi: 10.1109/ACC.1995.529214.
Figueroa, F., Schmalzel, J., Morris, J., Turowski, M., & Franzl, R. (2010). Integrated system health management: Pilot operational implementation in a rocket engine test stand. In AIAA Infotech@Aerospace. Atlanta, GA. doi: 10.2514/6.2010-3454.
Figueroa, F., Schmalzel, J., Walker, M., Venkatesh, M., Kapadia, R., Morris, J., . . . Smith, H. (2009). Inte- grated system health management: Foundational concepts, approach, and implementation. In AIAA In- fotech@Aerospace conference. Seattle, WA. doi: 10.2514/6.2009-1915
Frank, P. M. (1994). Enhancement of robustness in observer- based fault detection. International Journal of Control, 59(4), 955-981. doi: 10.1080/00207179408923112
Hattis, P., Campbell, D. P., Carter, D. W., McConley, M., & Tavan, S. (2006). Providing means for precision airdrop delivery from high altitude. In AIAA guidance, navigation, and control conference and exhibit. Keystone, CO.
Hattis, P., & Tavan, S. (2007). Precision airdrop. Aerospace America, April, 38-42.
Hwang, I., Kim, S., Kim, Y., & Seah, C. E. (2010). A survey of fault detection, isolation, and reconfiguration methods. IEEE Transactions on Control Systems Technology, 18(3), 636-653. doi: 10.1109/TCST.2009.2026285
Isermann, R., & Balle ́, P. (1997). Trends in the application of model-based fault detection and diagnosis of technical processes. Control Engineering Practice, 5(5), 709- 719. doi: 10.1016/S0967-0661(97)00053-1
Jones, H. L. (1973). Failure detection in linear systems. Doctoral dissertation, Massachusetts Institute of Technology, Cambridge, MA.
Patton, R. J., & Chen, J. (2000). On eigenstructure assignment for robust fault diagnosis. International Journal of Robust and Nonlinear Control, 10(14), 1193-1208. doi: 10.1002/1099-1239(20001215)10:14<1193::AID- RNC523>3.0.CO;2-R
Rossi, C. (2012). Vehicle health monitoring using stochastic constraint suspension. Master’s thesis, Massachusetts Institute of Technology, Cambridge, MA.
Rossi, C., Benson, D., Sargent, R., & Breger, L. S. (2012). Model-based design for vehicle health monitoring. In Infotech@Aerospace. Garden Grove, CA. doi: 10.2514/6.2012-2577
Rossi, C., Breger, L., Benson, D., Sargent, R., & Fesq, L. (2012). Vehicle health monitoring using stochastic constraint suspension. In AIAA guidance, navigation, and control conference. Minneapolis, MN.
Sargent, R., Mitchell, I., Breger, L., Benson, D., Bessette, C., Zanetti, R., & Groszkiewicz, J. E. (2011). A fault management strategy for autonomous rendezvous and capture with the ISS. In Infotech@Aerospace. St. Louis, MO. doi: 10.2514/6.2011-1497
Slegers, N., & Costello, M. (2004). Model predictive control of a parafoil and payload system. In AIAA atmosphere flight mechanics conference and exhibit. Providence, RI.
Sturza, M. A. (1988). Navigation system integrity monitoring using redundant measurements. Journal of the Institute of Navigation, 35(4), 69-87.
Tavan, S. (2006). Status and context of high altitude precision airdrop delivery systems. In AIAA guidance, navigation, and control conference and exhibit. Keystone, CO.
Van de Vegte, J. (1994). Feedback control systems. Prentice-Hall.
Ward, M., Montalvo, C., & Costello, M. (2010). Performance characteristics of an autonomous airdrop system in realistic wind environments. In AIAA atmosphere flight mechanics conference. Toronto, ON, Canada.
Willsky, A. S. (1976). A survey of design methods for failure detection in dynamic systems. Automatica, 12, 601- 611.
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