Reconfigurable Control Strategies: Towards Fault – Tolerant and High –

Confidence Systems

George VachtsevanosGeorgia Institute of Technology

Atlanta GA 30332-0250

SWAN ’06The University of Texas at Arlington

December 7 – 9, 2006

Flight Results – Bob Up

Collective Failure Scenario

•T = 0s

•T = 5s•Speed 25 ft/s

•Altitude 250 ft

Stuck Collective at Stuck Collective at point Bpoint B

Stuck Collective at Stuck Collective at point Bpoint B

A

CB

•Hover at 15ft

Without RPM controlWithout RPM control

With RPM controlWith RPM control

Man

eu

vera

bil

ity

Speed

Nominalcapability

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.

Degradedcapability

Human pilots

FTCReconfig-urable flightcontrol

Vehiclecapability

Human pilots

Ma

neu

ve

rab

ilit

y

Traditionalcontrolmethods Speed

Vehiclecapability

Human pilots

Ma

neu

ve

rab

ilit

y

Traditionalcontrolmethods Speed

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 sea30 mins into flight the helicopter with crew autorotates into the sea

“Retired Marine Lt. Gen. Bernard Trainor said the issue of aging aircraft is a constant complaint of all branches of service.”

Atlanta Journal ConstitutionApril 27, 2002

Aircraft Mishaps/Failure Modes

0 0.5 1 1.5 2 2.5 3 3.5

x 104

-1

0

1x 10

4 Simulated Vibration Signal

0 0.5 1 1.5 2 2.5 3 3.5

x 104

-5

0

5x 10

4 TSA of Vibration

200 300 400 500 600 700 800 9000

2

4

6

8x 10

7 FFT of TSA

1100 1110 1120 1130 1140 1150 1160 1170 11800

1

2

3x 10

7 Close-up view of Fifth Mesh Harmonic

0 0.5 1 1.5 2 2.5 3 3.5

x 104

-1

0

1x 10

4 Simulated Vibration Signal

0 0.5 1 1.5 2 2.5 3 3.5

x 104

-5

0

5x 10

4 TSA of Vibration

200 300 400 500 600 700 800 9000

2

4

6

8x 10

7 FFT of TSA

1100 1110 1120 1130 1140 1150 1160 1170 11800

1

2

3x 10

7 Close-up view of Fifth Mesh Harmonic

Testing/Seeded Fault Data Modeling

Reasoning Architecture for Diagnosis-Prognosis

IntermediateGearbox (IGB)fitted with VMEP sensors to monitor components

• Prevent unscheduled maintenance• Assist the pilot in making intelligent decisions about air-worthiness

VMEP/HUMSmodules

Testing, Modeling, and Reasoning Architecture for Fault Diagnosis and Failure Prognosis

The Fault Diagnosis/Prognosis Architecture

SystemSystemSensor Data

0 0 . 5 1 1 . 5 2 2 . 5

x 1 04

- 0 . 6

- 0 . 4

- 0 . 2

0

0 . 2

0 . 4

0 . 6

0 . 8

Sensor Data0 0 . 5 1 1 . 5 2 2 . 5

x 1 04

- 0 . 6

- 0 . 4

- 0 . 2

0

0 . 2

0 . 4

0 . 6

0 . 8

De-NoisingFeature

Extraction

Particle Filter

Preprocessed Data

0 0.5 1 1.5 2 2.5

x 104

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Preprocessed Data

0 0.5 1 1.5 2 2.5

x 104

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

PrognosisPrognosis

Particle Filter

Particle Filter

Experimental Data

0 0 . 5 1 1 . 5 2 2 . 5

x 1 04

- 0 .6

- 0 .4

- 0 .2

0

0 . 2

0 . 4

0 . 6

0 . 8

Experimental Data

0 0 . 5 1 1 . 5 2 2 . 5

x 1 04

- 0 .6

- 0 .4

- 0 .2

0

0 . 2

0 . 4

0 . 6

0 . 8

System Model for Diagnosis

McFadden

System Model for Diagnosis

McFadden

RULRUL

Simulated Data

Simulated Data

Noise ModelsNoise Models

Feature Extraction &

Mapping Techniques

Feature Extraction &

Mapping Techniques

Data Driven Methods

Data Driven Methods

De-Noising TechniquesDe-Noising Techniques

DiagnosisDiagnosis

HUMSHUMS

Offline Modules

Online Modules

Fault Growth

Flight Regime Data & Model Parameter

Tuning

Loading Profile Fault GrowthFault

Growth

Flight Regime Data & Model Parameter

Tuning

Loading Profile

System Model for Prognosis

Features & Mapping

0 1 2 3 4 5 6 7 80.2

0.4

0.6

0.8

1

1.2

1.4

1.6

crack length0 1 2 3 4 5 6 7 8

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

crack length0 1 2 3 4 5 6 7 8

0 .2

0 .4

0 .6

0 .8

1

1 .2

1 .4

1 .6

c ra c k le n g th

Features & Mapping

0 1 2 3 4 5 6 7 80.2

0.4

0.6

0.8

1

1.2

1.4

1.6

crack length0 1 2 3 4 5 6 7 8

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

crack length0 1 2 3 4 5 6 7 8

0 .2

0 .4

0 .6

0 .8

1

1 .2

1 .4

1 .6

c ra c k le n g th

Stress TableCrack Length Kmin Kmax

1.5 30.29 27.922 27.25 25.68

2.5 21.52 21.233 19.47 17.82

Stress TableCrack Length Kmin Kmax

1.5 30.29 27.922 27.25 25.68

2.5 21.52 21.233 19.47 17.82

Stress TableCrack Length Kmin Kmax

1.5 30.29 27.922 27.25 25.68

2.5 21.52 21.233 19.47 17.82

Crack Length Kmin Kmax

1.5 30.29 27.922 27.25 25.68

2.5 21.52 21.233 19.47 17.82

Space Engine Fault Accommodation

Body FlapControllers

ElevonControllers

RudderControllers

Component Degradation andSystem Performance Model

(From Task 2)

Prognostic & DiagnosticAlgorithms

(From Task 4)

Run-TimeDemo System

Actuator Commands

ActuatorPerformance Data

System Requirements(Task 1)

Model Validation(Task 3)

Integrated Flight Control System Logic(From Task 5)

Proposed Architecture

Mission AdaptationMission Assignment

FDIFDI

Baseline ControllerReconfigurable Flight Controllers:

coll

tr

aft

right

left

coll

tr

aft

right

left

coll

tr

aft

right

left

coll

tr

aft

right

left

Reconfigurable Flight Controllers:coll

tr

aft

right

left

coll

tr

aft

right

left

coll

tr

aft

right

left

coll

tr

aft

right

left

coll

tr

aft

right

left

coll

tr

aft

right

left

coll

tr

aft

right

left

coll

tr

aft

right

left

System ID

Reconfigurable Path Planner

Active System Restructuring

Baseline Path Planner

FAULT DECLARATION

FLIGHT PATH

FL

IGH

T P

AT

H

UAV MODEL

FL

IGH

T P

AT

H

CO

NT

RO

LS

UA

V L

IMIT

AT

ION

S

WA

YP

OIN

TS

WA

YP

OIN

TS

RE

ST

RU

CT

UR

ING

INS

TR

UC

TIO

NS

TIE

R 1

TIE

R 2

TIE

R 3

HUMAN INTERFACE

VEHICLE INTERFACE

CONTROLS

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: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

• 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

Mission 1 (Unmanned Supply Sustainment)

Fault Tolerant Control

(SSCI/GT)

FlightControl

Malfunction

Fly Autonomously (OGI-SDRE)

ExtremeManeuvers -

(Draper-AMGL)

PZ LZ

Mode Transitioning(GT)

Mission 1 (Unmanned Supply Sustainment):• Trajectory Generation (GT, Draper, SSCI)• Mode Transitioning (GT)• Fault Tolerance / Low Level Control (GT, SSCI, OGI)• Extreme Maneuvers (GT, Draper)

External Load

+

+

Adaptive NN Flight Controller(GT)

Reconfigurable Flight Control

Active Control:Active Control:

Longitudinal Cyclic lon

Lateral Cyclic latCollective Pitch coll

Tail Rotor Pitch trMain Rotor RPM com

Longitudinal Cyclic lon

Lateral Cyclic latCollective Pitch coll

Tail Rotor Pitch trMain Rotor RPM com

Baseline controller

Inverted Model

PD

Reference Model

RPM sensor:

Feedback Linearization:

-+com t Plant

AdaptiveNeuralNetwork

Flight Results - Stuck Collective

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 technologiesHigh Confidence Systems!

Intelligent Fault Diagnosis and Prognosis for Engineering Systems