1. Reconfigurable Control Strategies: Towards Fault – Tolerant and High – Confidence Systems George Vachtsevanos Georgia Institute of Technology Atlanta GA 30332-0250 SWAN ’06 The University of Texas at Arlington December 7 – 9, 2006

2. Flight Results – Bob Up

3. Collective Failure Scenario

4. Maneuverability Speed Nominal capability The Problem “Improving UAV reliability is the single most immediate and long reaching need to ensure their success.” - OSD UAV Roadmap 2002-2027 Unmanned aerial vehicles require a fault-tolerant control (FTC) architecture that allows them to generate and track safe flight paths before and after the occurrence of a fault. Degraded capability Human pilots FTC Reconfig- urable flight control

5. The Anatomy of a Failure Hydraulic fluid swapped for engine oil during maintenance More volatile lubricant evaporates increasing friction IGB output bearing overheats Bearing fails from excessive heat SH-60 loses tail-rotor authority SH-60 grounded for IGB servicing Hydraulic Fluid Runs Red by LCdr. Patrick Kennedy Mech, Winter 2001 30 mins into flight the helicopter with crew autorotates into the sea

6. “Retired Marine Lt. Gen. Bernard Trainor said the issue of aging aircraft is a constant complaint of all branches of service.” Atlanta Journal Constitution April 27, 2002 Aircraft Mishaps/Failure Modes

7. Testing/ Seeded Fault Data Modeling Reasoning Architecture for Diagnosis-Prognosis Intermediate Gearbox (IGB) fitted with VMEP sensors to monitor components Prevent unscheduled maintenance Assist the pilot in making intelligent decisions about air-worthiness VMEP/ HUMS modules Testing, Modeling, and Reasoning Architecture for Fault Diagnosis and Failure Prognosis

8. The Fault Diagnosis/Prognosis Architecture

9. Space Engine Fault Accommodation Body Flap Controllers Elevon Controllers Rudder Controllers Component Degradation and System Performance Model (From Task 2) Prognostic & Diagnostic Algorithms (From Task 4) Run-Time Demo System Actuator Commands Actuator Performance Data System Requirements (Task 1) Model Validation (Task 3) Integrated Flight Control System Logic (From Task 5)

10. Proposed Architecture

11. S/P Actuator A  A S/P Actuator B  B S/P Actuator C  C Tail Rotor Pitch  tr S/P Actuator A  A S/P Actuator B  B S/P Actuator C  C Tail Rotor Pitch  tr Helicopter Active System Restructuring RPM control –Collective –Tail rotor –Swashplate actuators Active Control:  com  coll S/P Actuator A  A S/P Actuator B  B S/P Actuator C  C Tail Rotor Pitch  tr Main Rotor RPM  com S/P Actuator A  A S/P Actuator B  B S/P Actuator C  C Tail Rotor Pitch  tr Main Rotor RPM  com Long. Cyclic  lon Lateral Cyclic  lat Collective Pitch  coll Tail Rotor Pitch  tr Long. Cyclic  lon Lateral Cyclic  lat Collective Pitch  coll Tail Rotor Pitch  tr Alternate means of restructuring employ: tandem rotors, stabilator control, individual blade control, jettisoning of stores  com Long. Cyclic  lon Lateral Cyclic  lat Collective Pitch  coll Tail Rotor Pitch  tr Main Rotor RPM  com Long. Cyclic  lon Lateral Cyclic  lat Collective Pitch  coll Tail Rotor Pitch  tr Main Rotor RPM  com Long. Cyclic  lon Lateral Cyclic  lat Collective Pitch  coll Tail Rotor Pitch  tr Long. Cyclic  lon Lateral Cyclic  lat Collective Pitch  coll Tail Rotor Pitch  tr

12. Adapts the position, velocity, acceleration, and/or jerk for the assigned waypoints Provides a simple exportable model (HURT) Implies a change to the aircraft time of arrival With or without reconfigurable path planning Mission Adaptation

13. Reconfigurable Flight Control Baseline controll er Inverted Model PD Referenc e Model RPM sensor: Feedback Linearization: - + Plant Adaptive Neural Network

14. Flight Results - Stuck Collective

15. Challenges for Control Engineers Robust, reliable and timely fault diagnosis and prognosis Interface requirements to system controllers System design to accommodate fault isolation, system restructuring and control reconfiguration Control reconfiguration technologies  High Confidence Systems!

16. Intelligent Fault Diagnosis and Prognosis for Engineering Systems