Boeing Defense, Space & Security
PhantomWorks
Autonomous Systems: The Future in
Aerospace
Kevin A. Wise, Ph.D.
Senior Technical Fellow, The Boeing Company
NAE-AAES Convocation
24 April 2017
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A Disruptive Surge in Autonomy
• New companies building
autonomous aircraft at record pace
• Strong competition to be first to
bring internet to new markets
• Ground/air package delivery
• Automotive industry moving quickly
• Predictions for 90% of Autos to be
autonomous by 2030
• Reliable low cost sensor
development
• Collaborative Autonomy
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From Piloted Stable Aircraft to Autonomous
Unstable Platforms: An Evolution
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1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Mechanical
Systems
Operated By
Pilot
Control
Nonlinear
Fly-By-Wire
Augmentation
Adaptive
Controls
Systems
Control
Autopilot
Reduces
Pilot
Workload
Mechanical To Digital
- Control Theory The Enabling
Technology
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What was the first UAV?
 First engine-powered, heavier-thanair aircraft capable of sustained flight
 Developed by Samuel P. Langley,
Secretary of the Smithsonian
 Langley Aerodrome No. 5
 First flights - 6 May 1896
 13 ft wingspan/tandem wing
 3300 ft and 2300 ft (1000 m and 700 m)
 Langley Aerodrome No. 6
 First flight - November 1896
 4790 ft (1460 m)
Aerodrome No. 5
Aerodrome No. 6
 Launched from a riverboat catapult
 Demonstrators for manned attempt
Ref: M. Hirschberg “American Attack Unmanned Aerial Vehicles,” AIAA 2003-01-3064
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Some Examples – Early Boeing Programs
 90 ft. wingspan
 One of the largest remotely piloted
jet-powered UAVs for its era
 Endurance of 17 hr 24 min at
55,000 ft. altitude
YQM-94A Compass Cope B
1973-1974
USAF photo
 All composite aircraft
 200 ft. wingspan (B-52 185 ft.)
 Modular construction for transport to
remote sites
 141 hr. flight test program
 Altitude record of 66,980 ft. for
piston-powered UAV
 Max duration of 2.5 days
Condor
1986 -1988
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Some Examples
 YPQM-149A or YPQM-150A
UAV- Short Range Sky Owl
 1989-1992
 Length 4.12 m (13 ft 6.2 in)
 Wingspan 7.32 m (24 ft)
Parafoil Autoland
 Weight 566 kg (1250 lb)
 Speed 204 km/h (110 knots)
All numbers are approximate.
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Some Examples
 31 flights with NASA
 Remotely piloted
 28% scale of manned fighter
 Controlled with forward canard, split
ailerons and thrust vectoring
X-36 Tailless Fighter Agility - Research Aircraft
1993-1998
NASA photo





 Additional flights under USAF
RESTORE program. First
demonstration of UAV adaptive flight
control.
RQ-3A DarkStar Tier III Minus
1996 - 1999
Fully automated flight using GPS
69 ft. wingspan
500 nm range with 8 hr loiter time
45,000 ft. altitude
Boeing/Lockheed Martin
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X-45 Joint Unmanned Combat Air System
X-45A
Phantom Ray
F-16
49 ft.
F-117
 Transformational Program
 Engineered To Support Full
Autonomous Operation
 Single Pilot/
Operator
Managing 4
Aircraft
36 ft.
Phantom Ray
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HALE – High Altitude Long Endurance
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





Aloft for 10+ days
65,000 ft
250 ft wingspan
Evolved from Condor
ISR
Telecommunications
Phantom Eye Demonstrator
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Intelligent and Autonomous Systems
• Interaction with humans
• Dealing with contingencies
• Trust
Sensing The
Environment
Machine Learning
Artificial Intelligence
Fusion,
Perception, and
Decision Making
Path Management
Navigation,
Guidance, Control
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Intelligent Autonomy – Evolutionary
Change Producing Revolutionary Capability
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 Robust system operation with/without
pilot/operator interaction
 Mission goals achieved in the presence
of system faults or contingencies
Plan
Find
Fix
Track
Target
Engage
Assess
Force Level
Planning/CAOC
Communicate
Execution
Chain
Flight Package
Real-time, Dynamic,
Optimal
“The whole is greater
than the sum of its parts”
Platform
Weapon/Sensor
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Integrated Battle Planning – “The whole is
greater than the sum of its parts”
Electronic Attack
ISR
Force Level
Planning/CAOC
Integrated Battle Plan
Flight Package
Platform
OCA/DCA
Strike
Weapon/Sensor
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Hierarchical Control in Intelligent/
Autonomous Systems
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Autonomous Flight
Machine Learning
Artificial Intelligence
In Outer Loops
Battle
Mgmt
KK5
K5K5
K5
Human
Operator/
Pilot Interaction
Multi-Ship
Mission Mission
Mgmt
Mgmt
Guidance/
Steering/
Navigation
∆
Flight
Control
Uncertainties
5
K4
K3
K2
P
K1
Contingencies and Uncertainties in All Loops
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Contingency Management - Critical for Safe,
Deterministic, Trustable Autonomy
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Detection
Air Vehicle
Report
MMS/MCS/FOCC
Sensor
Data
ISM
Input
Signal
Mgmt
RM
Redund
Mgmt
Air Vehicle
Monitor
Mission
Phase
&
Flight
Mode
Logic
Cont
Mode
Logic
Guidance
GPS
Navigation
Subsystem
Control
Channel A
Channel B
Flight
Control
OSM
Output
Signal
Mgmt
Subsystem
Cmds
• Air Data • Propulsion • Fuel
• ECS
• PDU
Cross
Channel
Data Link
Categorize
CM Database
Respond
System Response
Plans (SRP)
Engine Out
•
Bay Door Act
••
•Actuator Commands selected to be same value in both
channels
•Actuator failures will be detected to prevent force fights and
actuator damage using a combination of monitors
• Actuator
reports
failures of position, RAM,
•Actuator Commands selected
to be controller
same value
in both
ROM, EEPROM, power and communications errors
channels
• Software
model will
be fights
used to
compare model position
•Actuator failures will be detected
to prevent
force
and
to real actuator
position
actuator damage using a combination
of monitors
GCS Operator
TOSC
• Max rate
of change
for actuator
• Actuator controller reports
failures
of position,
RAM, position used to isolate
failures
of position data errors
ROM, EEPROM, power
and communications
in to
NAV
processor
willposition
be shut off if Flight Control
• Software model •Actuators
will be used
compare
model
processor in same VMS is failed
to real actuator position
•Actuators
in both
NAV and
Flight
Control processor will be
• Max rate of change
for actuator
position
used
to isolate
shut
off if a loss of CCDL is experienced but no loss in
failures of position
data
partner channel is detected (determined by loss of
•Actuators in NAV processor
will be shut
off ifstill
Flight
Control
communications
while
receiving
synch interrupt discrete)
processor in same VMS
failed
as is
Navigation
solution may be divergent resulting in actuator
•Actuators in both NAV
andfights
Flight Control processor will be
force
shut off if a loss of CCDL is experienced but no loss in
partner channel is detected (determined by loss of
communications while still receiving synch interrupt discrete)
as Navigation solution may be divergent resulting in actuator
force fights
• Deterministic & Predictable
• Validated, Tested, Rehearsed
MCC
GCS
• Independent Action,
Auto Response
• Operator Intervention
and Control Plan
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Why Is Trustable Autonomy Hard?
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Hierarchical, distributed, optimal control of manned,
semi-autonomous, and autonomous systems over
wireless networks in adversarial environments
Battle Mgmt and C2
Top
• Integrated Control of Distributed Assets
• Dynamic Resource Allocation
• Task Assignment, Scheduling, Route Planning, SAA
• Mission Execution and Monitoring
• Vehicle and Sensor Management (Outer Loop, Inner Loop)
Lowest
Level • Subsystem Management
Situational
Awareness
BMC2
Info
Mgmt
Multiship
Collaboration
Comm
Mgmt
Adaptive Networks
Challenges
SW Hierarchy, Complexity, Curse of Dimensionality
Dynamic Environment, Not Predictable
Contingency Management
Appropriate Models and Simulations
Non-unique Behaviors
Cyber Security
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Adaptive Control Motivation: Reduce Pilot
Workload In Controlling Damaged Aircraft
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• F-15 Accident Revives Interest In
Adaptive Control
• NASA IFCS (Indirect Adaptive)
• Adaptive Control Successfully
Applied To Open Loop Unstable
Systems
• USAF RESTORE (Direct Adaptive)
Closed-Loop
Reference
Model
A-300 Missile Attack
B-747 Engine Failure
F-15 Mid-Air Collision
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USAF/NASA/Boeing Strong Partners In The
Development of Adaptive Control
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Robust Adaptive Control
– Improved Performance, Mission Reliability
X-45A
– Reconfigurable, Damage Adaptive
– Retrofits Onto Existing Flight Control Laws
– Flight Proven
X-45C
Boeing/MIT/UIUC/CalTech
Technology Transition Timeline
96
97
98
Intelligent Flight
Control System
(NASA/Boeing)
F-15 ACTIVE
• Gen I, flown 1999, 2003
• Gen II, 2002 – 2006
•flight test 4th Q 2005
• Gen III, 2006-Present
99
00
01
02
03
04
05
Adaptive Control For
Munitions
(AFRL-MN/GST//Boeing)
MK-84
Reconfigurable
Control For Tailless
Fighters
(AFRL-VA/Boeing)
X-36
V&V methods for Adaptive
Systems
J-UCAS
06
07
08
09
10
11
12
13
14
Boeing IRAD
Dominator UAS
MK-82 L-JDAM
MK-84
L-JDAM
OBLTR
MK-84 JDAM
Phantom Ray
Indirect Adaptive
Control
Adaptive Dynamic Inversion (ADI)
MK-82 JDAM
MK-84 JDAM
IDP 2000
MRAC
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An Adaptive Control Challenge: Very
Flexible Aircraft
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 Output Feedback Control Architectures Needed For New VFA
Aircraft Designs
 Desire Model Based Flight Control Design
x =
Ax + Bu , y =
Cx
 Large Uncertainties Require Robust Adaptive Control
800 State Models
Flex Modes < 1 Hz
Helios
Big HALE
250 ft span
Large Deflections
Must Control Shape
Vulture
400 ft span
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Boeing Observer-Based Control with Loop Transfer
Recovery (OBLTR)
Dominator
Robust Adaptive Flight Control
X-45
•
•
•
•
HAAWC 6DOF
2015-2016
Improved Performance, Mission Reliability
Reconfigurable, Adaptive
Retrofits Onto Existing Flight Control Laws
Flight Proven
Phantom ICE
Technology Transition Timeline
12
13
14
15
16
MECC CRAD
GBU-57
MOP
2015-2016
Air Launch
CRAD 2016
DSTO CRAD
FF Feb 2015
SDB
17
uGCU
Dominator FF Nov 2013
TIGER CRAD
2015-2017
UFP CRAD
2015-2016
Scan Eagle 2016-2017
Pivot UAS
FF April 2014
IPODS CRAD
2015-2016
Proprietary
CRAD FF 2017
T3 6DOF IRAD
Copyright © 2014 Boeing. All rights reserved.
JDAM 2kER
2015
Adaptive Autopilot Technology
for Aerial Platforms
AFRL CRAD
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OBLTR Adaptive Control: The Algorithm
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Uncertainties
Cmds
P
Control
This method is a form of
Nonlinear Integral control
∆
Response
Adaptive
Gain
ymeas
Neural
Network
Lyapunov - The
Greatest Russian
Mathematician
ˆ T Φ xˆ , u
uad = −Θ
( bl )
ubl
Learning Rate Adjusts How Fast
The Adaptive Control Responds
Baseline
1
−

ˆ
T
Θ = −Γ Θ Φ ( xˆ , ubl ) e y R0 2 W S T
uad
Adaptive
ˆ 0 =
Θ
( ) 0
OBLTR Adaptive Increment
Pilot
Column/
Wheel Input
Or Guid
Cmds
Closed Loop
Ref Model
Aircraft
ŷ
Deadzone
+
-
ymeas
ey
If Aircraft Response Within Deadzone of
Desired Behavior Baseline Control Is Used.
If Error Increases Outside Deadzone
Nonlinear Integrator Kicks In To Adapt.
Form Error Signal Between Desired
Behavior and Aircraft
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Summary
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 100+ Years Of Unmanned Aircraft. There Here To Stay
– New roles/missions for commercial operation
– GAFA all developing capabilities
 Intelligent Autonomy
– Automotive industry developing autonomous capabilities at a fast pace
– Aerospace applications emerging
 Adaptive Control
– Improved safety if something goes wrong/fails
– Automotive and Aerospace applications emerging
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