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S. Ganguli, G. Papageorgiou, S. Glavaški, M. Elgersma Honeywell Advanced Technology GNC

Piloted Simulation of Fault Detection, Isolation and Reconfiguration Algorithms for a Civil Transport Aircraft. S. Ganguli, G. Papageorgiou, S. Glavaški, M. Elgersma Honeywell Advanced Technology GNC Presented by: G. Papageorgiou george.papageorgiou@honeywell.com SAE Conference October 2005.

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S. Ganguli, G. Papageorgiou, S. Glavaški, M. Elgersma Honeywell Advanced Technology GNC

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  1. Piloted Simulation of Fault Detection, Isolation and Reconfiguration Algorithms for a Civil Transport Aircraft S. Ganguli, G. Papageorgiou, S. Glavaški, M. Elgersma Honeywell Advanced Technology GNC Presented by: G. Papageorgiou george.papageorgiou@honeywell.com SAE Conference October 2005 #NCC-1-334 with NASA Langley Research Center #NAS1-00107 with NASA Langley Research Center

  2. Aircraft Control Surfaces • Commanded Control Surfaces (via autopilot): • Aileron Difference • Average Elevator • Rudder

  3. Piloted Simulation Setup

  4. IFD vs Matlab: Comparative plots

  5. CUPRSys Overview

  6. CUPRSys Algorithms – Aircraft Model • Express aircraft dynamics as sum of nominal nonlinear function and a linear combination of (nonlinear) basis functions. • Aircraft equations:

  7. Flight Conditions Reduction of effectiveness faults and various maneuvers

  8. Matlab Simulation (Low Cruise Pitch Down) Pilot modeled by Prop. Gain - No Fault -. 75% Fault .. Reconfigured - Command

  9. Flight Card

  10. Low Cruise – 10 deg Pitch Down (No fault)

  11. Low Cruise – 10 deg Pitch Down (75% e fault)

  12. Low Cruise – 10 deg Pitch Down (Reconfiguration) Larger command

  13. Low Cruise – 10 deg Pitch Down (Reconfiguration) Smaller command

  14. Cooper Harper Ratings & Pilot Workload - LC (5 rad/s) (2 rad/s)* LP || ||2 HP Low Cruise * R. Mercadante, “Piloted Simulation Verification of a Control Reconfiguration for a Fighter Aircraft under Impairment”, AGARD No. 456, Toulouse, France, 1989

  15. FDI Performance • Performance measured by: • False-alarms • 1 LC pitch-up maneuver, and during flare tasks (ground effects not modeled?) • Missed detection (none, but sensitivity to small faults not tested) • Accuracy of identified fault

  16. Lessons Learnt and Recommendations • Limitations • CUPRSys uses  and  sensors – typically not available • Feel system model not available for design • Current deficiencies of CUPRSys • On-board aircraft model uses exact replica of Engine Model • H-matrix and Threshold Functions vary with flight condition • Gain reconfiguration vs control re-allocation • CUPRSys restricted to gain reconfiguration (commanding through autopilot) • Control authority of additional surfaces can restore flying qualities

  17. Conclusions & Future Work • Piloted simulations conducted at LaRC suggest • Robust control law • Promising FDIR capabilities (need more validation sims with control re-allocation) • Future Work • Utilize control allocation • Accommodate sensor dynamics and noise • Accommodate turbulence • Expanded set of failures

  18. Pilot Cueing

  19. Integration Flight Deck (IFD) • Piloted Simulations were conducted at the LaRC IFD facility.

  20. Acknowledgement • Thanks to the NASA Team for their support, encouragement and various helpful discussions: • Pat Murphy • Steve Derry • Gus Taylor • Rob Rivers • Tom Bundick • Christine Belcastro

  21. www.honeywell.com

  22. NASA Aviation Safety & Security Program • NASA AvSSP • $500 million* • Reduce commercial aviation accident rate by 80% by 2007* (* http://www.nasa.gov/centers/langley/news/factsheets/AvSP-factsheet.html) http://avsp.larc.nasa.gov/program.html

  23. AMASF Program Overview • Phase I (mid-sized commuter aircraft) • FDI technologies for selected failures (stuck/floating actuators, reduction of control surface effectiveness) + icing • Pilot Cueing strategies • Control Reconfiguration • Phase II (mid-sized civil transport aircraft) • Transition of algorithms to new aircraft • Algorithms + display integrated to CUPRSys • Failure type: reduction of control surface effectiveness • Phase III • Piloted simulation at LaRC

  24. CUPRSys Algorithms – Reconfigurable CLAW • Based on Dynamic Inversion • Desired Dynamics • Control Law (under certain assumptions) + feedforward DI P + I K

  25. CUPRSys Algorithms – Reconfigurable CLAW • Controller bandwidths • High dynamic pressure (High/Low Cruise): • [p C*]= [2.0 1.25 1.0] rad/s • Low dynamic pressure (near Approach): • [p C*]= [2.5 0.75 0.75] rad/s • Inceptor Scalings • Wheel (85 deg): 0.25 (deg/s)/deg • Column (-9.2 to 13.3 deg): 2.00 (deg/s)/deg • Pedal (4 inch): 0.02 rad/inch • Anti-windup (software limiting) fc Inversion Kb fiKb 1/s Kaw sat lim

  26. CUPRSys Algorithms – Fault Detection • Residual Generator Angular Acceleration Estimator Scaled, Added Noise Rejection • Threshold Function abs( ) LP Scaled, Added Bias Turbulence Rejection

  27. CUPRSys Algorithms – Fault Isolation • RLS Estimator: After acquiring k samples: Over-determined linear algebra problem: Weighted Least Squares problem: Solved using standard RLS Estimation algorithm with forgetting factor.

  28. CUPRSys Algorithms – Fault Isolation • H-Matrix Convergence Criterion • H-Matrix Update • H-matrix for FD (Residual Generator) • H-matrix for CLAW • Signal Injection • Trade-off between sufficient excitation time and quick FDIR • Simultaneous doublet commands (4 sec) in all three axes • 0.5 deg/s p • 0.5 deg/s C* • 1 deg  and

  29. High Cruise – 10 deg Pitch Down (No fault) + light turbulence

  30. High Cruise – 10 deg Pitch Down (75% e fault)

  31. High Cruise – 10 deg Pitch Down (Reconfiguration)

  32. Cooper Harper Ratings & Pilot Workload LP || ||2 HP High Cruise

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