Transcript Barron Associates, Inc. Selected Current Research

Barron Associates, Inc.
Selected Current Research
SAE International
Aerospace Control & Guidance Systems Committee
Niagara Falls, NY
October 14, 2008
David G. Ward
(434) 973-1215
[email protected]
Proprietary
ACGSC Meeting 102 – Grand Island, NY
October 15, 2008
IAG&C for Reusable Launch Vehicles
AFRL Programs / Flight Phases
IAG&C for Ascent
Working with:
Status:
IAG&C for Re-entry
Prof. Ping Lu
• High fidelity 6-DOF sim dev. (Northrop)
• Reconfig. controller developed (AFRL)
• Adaptive guidance matured (BAI)
Program Objectives:
• Successfully recovers / reshapes
• Adaptive ascent
guidance
trajectory
to engine outs, other
st
nd
• Recover both failures
1 & 2 stages under
engine and/or
actuator
failures
• Final
Review
in November
IAG&C for Rapid Mission Planning
Working with:
Status:
Prof. Ping Lu
Matlab/Simulink
• Significant tool maturation Prof. Craig Kluever
• Prototype demonstrated Java
JavaUser
User
Program
Objectives:
Interfaces
Interfaces
• Lockheed to aid in
• Develop Mission Planning tool for
final demonstration Database
RLVs
C/C++
• Work to continue in Management
• Rapid mission planning capability
follow-on Phase III
• Launch ready within 2 hours, 24/7
effort
Proprietary
Working with:
Status:
• Reconfig. controller developed (BAI)
• Re-entry trajectory command
generationObjectives:
developed (BAI)
Program
• •Successfully
recovers
/ reshapes
Adaptive re-entry
guidance
lift & drag
variations
•trajectory
Recovertovehicle
under
• Boeing
to test
robustness in high
actuator
failures
fidelity dispersion studies
• Final Review in December
Future Access to Space Technology
(FAST)
Working with:
Status:
• Configuration continues to be
developed (Northrop. Lockheed, Honeywell)
• Aerodynamic model development
Program Objectives:
continues (Northrop, Honeywell, AFRL)
• Apply adaptive
guidance
• ICD
near completion (Northrop, Honeywell,
technologies toBAI)
FAST concept
vehicle
Innovative Rotorcraft Control for
Shipboard Operations
NAVAIR SBIR Phase II
TPOC: Mr. Dean Carico
Expand operational envelope of rotorcraft from
aviation capable ships
• Turbulent environments
• Ship motion
• Rotorcraft/Ship combinations
• Airwake effects
Dr. Joseph F. Horn
PSU Vertical Lift Research Center
of Excellence
Adaptive and Learning Control
Real-time implementation & evaluation
Estimate disturbances and reduce pilot workload
Pilot Attitude
Command
Stochastic
Disturbance
Rejection
Time-varying
deterministic
approximation
Least Squares Fit
FFT of Simulation Data
AR Model
2
Approx. Lateral Wind
Normalized
Autospectra for roll gust, p
g
1.5
0
10
-2
10
-4
1
0.5
0
-0.5
0
10
-200
-20
-300
-6
10
-1
10
0
1
10
10
Frequnecy (rad/sec)
Proprietary
Ideal
Response
Model
Pseudocontrols
PID
Comp.
Actuators
Inverse
Dynamics
Airwake
Feedback
Compensation
Adaptive
Algorithms
Stochastic Spectral
Estimation
10
Feed-forward Trim
Compensation
Trim
Compensation
-400
-40
2
10
-500
Zpos, ft
-60
-600
Xpos, ft
Rotorcraft
Flight
Dynamics
Sensor
Data
Autonomous Collision Avoidance and
Separation Assurance for
Small UAVs in the NAS
Damage Adaptation using Integrated
Structural, Propulsion,
and Aerodynamic Control
Novel Collision Avoidance:
Improved Aviation Safety:
• Spenko, Dubowsky (MIT, 2006)
• Compensate catastrophic damage
(structure, propulsion, effectors, sensors)
• Very low computational burden
• Strong safety guarantees
• Robust to large uncertainties
• Dynamic model-based
Approach:
Trajectory space formulation
dramatically reduces burden
• On-line adaptation of subsystem design specs
• Managed through smart, V&V’able middleware
Phase II Objectives:
Phase I Objectives:
• Develop design-time tools to facilitate spec integration
• Integrate CA with BAI
path planning algorithms
• Develop run-time middleware to adapt/manage specs
• Demo on representative surrogate platform
• Quantify processing
& sensing requirements
• ID HW for Ph. II demo
On-line adaptive specs
Proprietary
ACGSC Meeting 102 – Grand Island, NY
October 15, 2008
Advanced V&V Technologies
AFRL’s FCSSI Program: CerTA FCS, MCAR, CPI & TASS SBIRs
2
Background
Example Degraded Mode
Safety Wrapper
1
Safety
Wrapper 2
Runtime Verification
& Validation
(RTVV)
• Monitor high risk S/W
Modulein
1 flight (algorithm/associated
Module 2
code that cannot be fully certified a priori due to
Backup 1
Backup 2
pper 3
advanced technologies)
3 • Shut down high risk S/W if anomalous behavior
Safety Wrapper 3
Problems Detected
observed
3
in Modules 1 & 2
Module
3
• Revert to simplified (standard/classical)
backup
mode (can be certified at design time)
Backup 3
• Return to base/recover vehicle safely
Mixed Critical Architecture Requirements
(MCAR) Working with:
Program Objectives:
Status:
•
•
•
•
Developed
Develop
requirements
tool to generate/organize
for mixed critical
requirements
flight
systems list of requirements generated
Prototype
Focus on safety & security
S2 integration
M
M2
Barron Assoc. – focusS3on RTVV
into
mixed critical architectures
Middleware Layer
S
Proprietary
HCRTOS
TASS SBIR Phase III
Working
Status: with:
• RTVV approach greatly matured
Program
Objectives:
• Integration
into high fidelity triplex system – working
• Mature
RTVV
w/Lockheed system
•• Integrate
RTVV
Design time
cert.into triplex system with RM
• Certify
RTVV
techniques
forsystem
RTVV at design time
• Mature
Flight critical neural network verification tool
investigated
•• Lockheed
Lockheed to
to test
soonsystem in real-time simulations
begin real-time testing
3x1
VMC-OFP
VMC-OFP
RM
output
selector
CCDL
2
(cross channel data link)
pper 2
FDI
input
selector
Actuators
FLCS
Performance
Safety
VMC-OFP
3x1
3x1
SBE
electronics
sensors
Includes
actuator health
signal used by
input selector,
FDI and FLCS
Challenge Problem Initiative (CPI)
Working
Status: with:
• Challenge problem selected: QF-16 (unmanned F-16
Program
Objectives:
drones) autoland
system certification
•• Apply
FCSSI
technologies
to a particular
challenge
Focus on actual incident: incomplete
mode
logic
problem
resulted in hard landing during flight test
•• Barron
Assoc.
– focus
on RTVV
integration
into
Developing
MoMs,
KPPs
to measure
cost savings
of
chosen
challenge
problem
certifying autoland with new methods
• RTVV application: developing
safety corridor & trajectory
prediction – is A/C currently safe?
Polynomial Chaos Uncertainty Tools for Flutter
• Develop methods for “non-intrusive”
use of polynomial chaos
• Fitting polynomial chaos
representations to empirical data
• Leverage domain knowledge to reduce
complexity of fitting problem
• Address challenges of representing
uncertainty in very high order models
Polynomial Chaos Fit to
Eigvenvalue in Aeroelastic Model
Proprietary
Automated Updates of Tiltrotor Simulations using
Experimental Data
NAVAIR SBIR Phase I
TPOC: Mr. Sean Roark
Automate simulation-updates from experimental data
•
•
•
•
•
Assist analyst in knowing where to update simulation
and what the update should be
Structure learning
System Identification
Incremental database updates
Statistically justified and local updates
Phase I Results
• Data preprocessing (smoothing)
• Frequency domain parameter estimation
• Identify model structure for coupled, nonlinear
effects
aeronautics.arc.nasa.
gov
-
halfdome.arc.nasa.gov
Simulation Update Process
• Overcome correlated actuators
• Rigorous statistical fusion of parameter
estimates
1. automatically determine nonlinear
regression structure at a particular
condition
5. automatically update simulation data
based upon analysis
Pitch Up with Sideslip
Heave-Roll (XV-15 ground effect)
0.6
nonlinear terms
(e.g., splines)
M
C
( a , Mach ,...) = z
i
i
Simulation
M
= C
M
+ ... + C
0
... + C
Data Tables
M
a1
a +
- 40 ) 2 ] +
[( a
0
2. Perform regression on
data
4. convert to form
suitable for
simulation data
table
Proprietary
Convert
Convert to
to
aero
aero table
table
format
format
=
Ma
1
 N (0 ,s
C
m
M
a1
Experimental
Flight
Data
Data
M
0.2
Ma
L
C
Truth
Estimated
0.4
-0.2
Improved fit using
identified model structure
-0.4
)
a1
-0.6
3. compute confidence measures for the
parameters that will be used to update
the database
-0.8
0
10
20
30
40
50
Time, sec
60
70
80
90
100
Autonomous Operations in Riverine Environments
Unmanned Underwater Riverine Craft
Operations
Specific mission not defined. Capabilities include:
Intelligence, Surveillance, and Reconnaissance (ISR) class of
operations
 Persistence
 Deploy/Retrieve
 Identification
Search, “leave behind”, etc.
Proprietary
Riverine Environment
Tidal wave and river current interactions
Depth variation/stratification
Confined navigation
Low visibility
Traffic
Obstacles
Automated Upset Recovery System
for Unmanned Air Vehicles
Automated Recovery System
Unusual Attitude Recovery System
Inner-Loop
Control
RL Module
OOC Arrest System
Actuator
Commands
RL Module
Out-of-Control Arrest System
•
Reference
Guidance and
Control Law
Unusual Attitude Recovery System
•
Phase II objectives:
robust approach for arresting large angular rates in
nonlinear flight regimes
modify commands/gains to inner-loop control to recover
from early-onset upsets and unusual attitude situations
Develop upset recovery
methodology
Conduct HWIL/flight test
demonstration
Demonstrate approach in
simulations
Develop tools to automate
recovery capability
A
Proprietary
B
Proposal
T2.02-9831
NASA SBIR/STTR Technologies
Active Flow Control with Adaptive Design
Techniques for Improved Aircraft Safety
PI: Jason Burkholder / Barron Associates, Inc. – Charlottesville, VA
Significance of Opportunity
•
Potential for low-cost safety improvements for
commercial transport aircraft
 Innovative synthetic jet actuators strategicallylocated on airfoil could delay stall and provide
“back-up” control power
 Adaptive control is required due to complex,
nonlinear actuator dynamics
Phase II Actuator Designs
Phase I Results
•
Designed and implemented adaptive control laws –
verified performance analytically and in simulation
Designed wind tunnel model, novel actuators, and
comprehensive Phase II test plan
•
Phase II Wind Tunnel Model Design
Phase II Work Tasks
•
•
•
•
Develop fully functional AIFAC tool (Adaptive Inverse For
Applications
•
AirSTAR Testbed for AvSP/SAAP
Actuator Compensation)

Fabricate wind tunnel models and synthetic jet
actuators – optimize actuator layout
Implement real-time adaptive control system and
demonstrate in closed-loop wind tunnel tests
Quantify safety improvements and develop V&V Plan
to facilitate future flight tests

•
•
Complex damage-adaptive FDI & control
Operation near edge of flight envelope
NASA Intelligent Flight Control System (IFCS)
Commercial and military aircraft – especially tailless
“stealth” aircraft
Contacts
[email protected]
(434) 973-1215