Development, Instrumentation, and Flight Testing of UAVs as

Transcript

Development, Instrumentation, and Flight Testing of
UAVs as Research Platforms for Flight Control
Systems Research
by:
Marcello R. Napolitano, Professor
Flight Control Research Laboratory, Director
( http:/fcrl.mae.wvu.edu )
Department of Mechanical and Aerospace Engineering
College of Engineering and Mineral Resources
West Virginia University
Universita’ di Pisa
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Introduction to WVU UAV Research Program
WVU Flight Control System Laboratory (FCSL) Group
Marcello R. Napolitano – Professor
Dr. Mario Perhinschi – Associate Professor
Dr. Yu Gu - Assistant Professor
Dr. Brad Seanor - Research Assistant Professor
Dr. Srikanth Gururajan, Dr. Haiyang Chao – Post Doctoral Research Fellows
Tanmay Mandal, Trenton Larrabee, Matthew Rhudy, Caleb Rice, James Reil - Graduate Students
Sean Bilardo, Clinton Smith, Matthew Milanese, Matthew Underwood – Undergraduate Students
with the collaboration of:
Mike Eden, Mike Spencer – Research Pilots
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Introduction to WVU UAV Research Program (cont.)
Available Research Facilities for UAV and Flight Controls Research
- “Flight Simulation Laboratory”
- “Motion-Based Flight Simulation Laboratory”
- “Model Construction / Avionics Laboratory”
- “Flight Testing Research Facility” (WVU Jackson Mill)
Model Construction / Avionics Laboratory
Lab facility used for:
- design and manufacturing of customized UAV models;
- design and manufacturing of customized avionic payloads for UAVs.
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WVU Flight Testing Facility:
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WVU owned facility at Jackson’s Mill
Louis – Bennett Airfield, located approx. 65 miles south of
Morgantown, in Jane Lew, WV
Features a 3,300 x 50 ft paved airstrip
Nested in a valley, away from population centers
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Flight Simulation Laboratory
featuring:
- 16 ‘double monitor’ PC-based flight simulation stations (featuring “D-Six” simulation package)
- 6 Degree of Freedom Motus 3600 Motion Based Flight Simulator
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Presentation Outline
- Basic Design Issues for Research UAVs
- Review of WVU Capabilities in UAV Design and Flight Testing
- Review of WVU UAV research efforts
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Basic Design Issues
for Research UAVs
WVU YF-22 Research Aircraft
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Basic UAV Design Issues / Questions
.. A typical starting point !
- .. What
is the operational flight envelope required for the UAV ?
-.. What aircraft performance will be required for a specific
mission?
..autonomy ?? … speed ?? … range ?? .. Payload ??
structural g’s ??
-.. What sensors are required for the mission roles? Are the sensor package
modular ? … issues of modular payload packages for multiple purposes
UAVs ?
- .. What is the weight of the required payload ?? .. What are the power
requirements for the required payload ??
- .. What are the aircraft power requirements ?? .. Jet propulsion ?? ..
Propeller ?? .. Electric propulsion ?? .. Solar propulsion ??
- .. What are the difficulties in construction and fabrication?
- .. Is COST a design issue ?? … is WEIGHT a design issue ??
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UAV Design Lessons Learned at WVU
Lesson #1
.. A magic formula/approach does not exist ! Ultimately, as for the
design of manned aircraft, the design will be a trade-off between
several factors, such as weight, cost, complexity, and many others.
Lesson #2
.. A detailed evaluation of the requirements is even more important
for the design of UAVs than in the design of manned aircraft.
Lesson #3
.. It is virtually impossible to find a ‘perfect UAV design’ for a given
mission. The “optimal” – but not the most time efficient and cost
effective – approach is to design a UAV around its payload for a
given set of requirements.
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Basic UAV Design Cycle
Typical set of REQUIREMENTS (from sponsor/customer) :
-Mission Profile
(in terms of altitude, range of airspeed, flight envelope,
turning radios, sustainable g’s, …)
-Payload Requirements
(in terms of weight and other potential issues, such as EMI)
-Range and/or Autonomy
(in terms of flight time and/or maximum traveled distance)
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Basic UAV Design Cycle (cont.)
Requirements
Mission
Profile
Payload
Requirements
Range
Autonomy
Estimate
TOW
PROPULSION
System
FUSELAGE
Design
WING Design
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TAIL
Design
Selection of the PROPULSION System
It is typically dictated by Mission Profile requirements (in terms of maximum
and/or minimum airspeed, flight envelope,..). Depending on the selection (jet,
propeller, solar, electric), this selection has a main influence on the estimate of the
Take Off Weight (TOW) – AND – on the general wing/fuselage design.
A number of commercial solutions are available for each type of propulsion
system.
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Potential issues for the propulsion system
- Engine and fuel weights are quite large percentages of the TOW;
- Lack of large number of propulsion options
(for example, commercial jet engines are typically available for
25 lbs, 50 lbs, 100 lbs thrust);
- Customized fuel tanks are typically needed for storing fuel in the wings.
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Initial estimate of the TOW & (T/W)
From the Requirements, following the selection of the type of propulsion system
(along with the estimate of the engine and fuel weight), an initial estimate of the
take off weight (TOW) Next, the sizing of the propulsion system has to satisfy the
following critical design parameter:
T / W : Thrust-Weight Ratio
Range : T/W ~ [0.35 – 0.65]
Low T/W values imply lower fuel consumption (> lower fuel weight), long takeoff distance, generally lower maneuverability, etc.
High T/W values imply higher fuel consumption (>higher fuel weight), short takeoff distance), generally higher maneuverability, etc.
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WING Design
Just like manned aircraft, by far the single most critical component of the
entire UAV design.
A general rule of thumb is that wings for small UAVs need to have fairly
high tip ratios, fairly high aspect ratios, and fairly small sweep angle – since
they operate at low speed (Mach < 0.1).
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Main Parameters for WING Design
- Wing load >> Wing Surface
- Landing speed
- Structural strength
- Construction and manufacturing issues
- Transportation and storage issues
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Wing Load
Wing Load (W/S = Weight Wing Surface ratio) is the MOST critical
parameter for wing design.
For small size UAVs is estimated in terms of ‘ounces / square feet ‘.
Range: W/S ~ [40-80] oz/sqf.
Higher W/S values are associated with undesirable handling qualities.
Once a ‘target W/S’ is selected, the associated wing surface (S) is calculated.
NOTE: 1 lbs = 16 oz ~ 450 gr.
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Wing Planform
For a given wing surface, the selection of the wing planform has to
emphasize the need for high tip ratios, high aspect ratios, and negligible
sweep angles necessary for low Mach numbers. Typical ranges for these
parameters:
Tip Ratio ~ [0.5-0.8]
Aspect Ratio ~ [5-8]
Sweep Angle ~ [0-20] deg.
SPECIALE CASE
If a customer/sponsor requires the UAV to resemble a specific manned
aircraft, the wing surface has to be increased and/or the above parameters
need to be modified with respect to the wing of the manned aircraft.
Therefore, it is impossible to build UAVs as scaled model of manned
aircraft.
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Landing Speed
The landing speed can be a requirement induced by several factors, such as:
- level of training of the UAV pilots;
- type of deployment of the UAV and available facilities;
- type and strength of landing gears;
- allowed level of g’s sustainable by the payload;
- …others.
Experience UAV pilots can perform landing without an on-board camera up to
90 kmh (~60 mph). Thus, a good rule of thumb is to keep the landing speed
below 90 kmh (~60 mph). Higher landing speeds are possible with an onboard camera.
Selection of wing sections with high cambers (curvature) and/or the
use of trailing edge flaps are the main features for controlling landing speed.
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Additional Wing Design Parameters
Structural strength, construction and manufacturing issues, transportation
and storage issues are case-by-case dependent.
If cost is a issue, less expensive material (for example, fiber glass, foam,
wood) can be used for most of the wing while more expensive material (for
example, carbon fiber) can be used for specific sections of the wing (for
example, around landing gears, wing-fuselage intersection.
Foldable, sectional wings, and/or mountable wings are appealing solutions
for transportation and deployment purposes. Several common requirements
call for 10 minute UAV deployment time for a 2-person crew.
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FUSELAGE Design
Although less critical than WING design, FUSELAGE design is still
an important aspect. In general FUSELAGE is used to carry the
mission payload since UAV wings are typically not structurally
for carrying meaningful payloads. Specific issues in FUSELAGE
design are:
- structural strength of wing/fuselage intersection;
- structural strength of landing gears/fuselage intersection;
- structural strength of payload cargo bay areas;
- structural strength of the engine mounting (for internal propulsion
system).
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FUSELAGE Design (cont.)
From a payload point of view, the following issues are critical:
- Appropriate balancing of the payload/fuel in the fuselage for a
very accurate estimate of the UAV center of gravity (CG). Note that
for a small UAV the static margin should be in the range of [1-3] in.
- Management of limited fuselage volume for storing engine fuel,
batteries, and potentially delicate components of the payload.
- Acceptable levels of vibration and/or accelerations.
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FUSELAGE Design (cont.)
A potentially very important problem – only noticed after the
installation of avionic payloads – is the occurrence of excessively
high levels of EMI (Electro Magnetic Interference) between different
components.
GOLDEN RULE: EMI issues are best when prevented !!
Following EMI occurrence a solution will involve a combination of
sealing of electronic packages with aluminum/cupper tapes, use of
ferrite chokes, and – if necessary – reallocation of specific EMI
generating components within the fuselage.
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WVU Capabilities in UAV Design,
Instrumentation, and Flight Testing
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WVU Capabilities in UAV Design,
Instrumentation, and Flight Testing
• 20-year old program.
• 16 different research platforms.
• Currently involving approx. 15 researchers (faculty, research associates,
students)
• Approx. $10 M funding (NASA, USAF, US Army, US Navy)
• > 600 flight testing experiments to date.
• In-house capabilities for design, development, manufacturing, and
instrumentation of UAVs with a range of payloads
•WVU-owned flight testing facility (Jackson’s Mill, WV)
• > 140 technical publications
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WVU UAV Design and Manufacturing
Composite Structures
Wood Structures
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WVU UAV Propulsion Systems
“Turbine” Engine Specifications
– Maximum Thrust:
– Max. Fuel Consumption:
RAM 1000
Jetcat P120SX
28 lb.
30 lb.
12 oz./min
12 oz./min
– Maximum RPM:
126,000
126,000
– Mission Duration:
~9 min
RAM 1000
~9 min
Jetcat P-120-SX
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WVU UAV Propulsion Systems (cont.)
“Electric” Engine Specifications
– Thrust Output: ~9 lbs. (2 motor config.)
– Input Voltage:
22 V
– Current Draw: 60 A
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WVU UAV Propulsion Systems (cont.)
“Glow” & ‘Gas” Engine Propulsion Systems
GMS 0.76 2-Stroke Glow Engine
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DA-100
WVU UAV Labs & Facilities
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WVU Flight Testing - Field Support
• “Field Trailer” used for transport of unmanned
vehicles and support equipment;
• Support: Work station / Weather instruments /
Communications / Field equipment
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WVU Flight Testing Facility
• WVU Jackson’s Mill (Louis-Bennett Airfield)
located near Jane Lew, WV;
• Located 65 miles south from the WVU campus;
• University owned facility;
• 3,300 ft. paved runway;
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History of WVU UAV Program
1994-1997
1998-2001
2001-2003
1/24 Scale B747 Model
1/24 Semi-Scale B777
Model
1/10 Semi-Scale YF-22
Model
Ducted fan propulsion
Jet propulsion
NASA Project on Fault
Tolerant FCS
USAF Project on Fault
Tolerant FCS
Ducted fan propulsion
NASA Project on Fault
Tolerant FCS
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WVU B777 General Description
length
8.75 ft
b (Span)
8.92 ft
(Taper Ratio)
0.27
Cr (Root)
2.00 ft
Ct (Tip)
0.54 ft
LE
Aspect Ratio
S (Wing Area)
27.0 deg
7.02
11.33 ft
2
Mean Aerodynamic Chord
1.41 ft
Elevator total area
0.48 ft
2
Aileron total area
0.64 ft
2
Rudder total area
0.33 ft
2
Elevator span (left & right)
2.64 ft
Aileron span (left & right)
2.67 ft
Rudder span (left & right)
1.46 ft
NOTE: The wing planform was substantially
modified with respect to the ‘actual’ B777
for improving aerodynamic performance at low speed.
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WVU “Original” YF-22 UAV
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WVU UAV Fleet
Cessna 152
Lancair
Wing Span: 119”
Length: 87”
Weight: 36 lbs.
Wing Span: 80”
Length: 52”
Weight: 9.4 lbs.
Low speed with large
payload capacity and
extended mission endurance
Low cost, fast deployment,
flexible research platform
with small payload capacity
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WVU UAV Fleet (cont.)
Mig-27 Foamy
Bergen Industrial Twin
Wing Span: 66”
Length: 71”
Weight: 10 lbs.
Main Rotor Span: 64”
Length: 59”
Weight: 18 lbs.
Low cost, rugged research
platform with medium
payload capability
Rotary wing platform with
heavy payload capacity for
missions requiring VTOL
capabilities
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Propulsion Assisted Control (PAC) Test Bed
Aircraft Specifications
– Wing Span:
~ 96 ”
– Length:
~ 84 ”
– Weight:
~ 22 lbs.
– Payload:
~ 7 lbs.
Engine Specifications
– Thrust Output: 9 lbs. (2 eng. config.)
– Input Voltage:
22 V
– Current Draw: 60 A
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Propulsion Assisted Control (PAC) Test Bed (cont.)
Key Features:
• All composite construction
• Low-cost and modular design
Engine Attachment Points
• Reconfigurable propulsion system
Configuration A
Configuration B
• Rotating shaft for longitudinal thrust
vectoring capabilities
Configuration C
Configuration D
• Uses high bandwidth brushless
electric ducted fans (EDF) for
evaluation of effects of engine
dynamics for flight control purposes
• Provide forward, braking, and
control forces for a concept
hybrid aircraft.
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PAC Test Bed Construction
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Flagship Aircraft: Fleet of 3 WVU YF-22
–
–
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–
–
–
Length:
8 ft.
Wing span: 6.5 ft.
Wing area: 14.7 ft2
Payload: ~12 lbs.
TOW:
50-54 lbs.
T/W ratio: [0.5-0.55]
Engine Specifications
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– Maximum Thrust:
28 lb.
– Fuel Consumption:
12 oz/min
– Maximum RPM:
126,000
– Mission Duration:
12 min.
Typical WVU UAV Sensor Packages
Suite of on-board sensors
– Standard (traditional) suite of aircraft parameters:
• air-data / flow measurements
• accelerometers / angular rates (IMU)
• Euler angles / heading
• control surface deflections / pilot (and/or) command inputs
• GPS (velocity / position)
Additional (optional) sensors
•
•
•
•
•
structural information
engine parameters / fuel indicators / aircraft status parameters
camera and/or video systems
Communication systems
Chemical/atmospheric sensors
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Evolution of WVU UAV Flight Computers
Gen I: Basic Data Acquisition (6 lb.)
Gen II: PC104 with Data Acquisition (2.6 lb.)
Gen III: PC104 with Flight Control (2.4 lb.)
Gen IV: Stand-Alone Autopilot (3 Oz.)
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Evolution of WVU UAV Flight Computers
Gen-V Avionics (see later sections)
Key Features:
•
•
•
•
•
•
Independent control of 9 channels;
Dual R/C receiver configuration;
EKF based GPS/INS sensor fusion;
Support Matlab® Real-Time Workshop;
800MHz processor;
Moderate size (6×5×3”) and weight (~3 lbs).
Operational Modes:
•
•
•
•
•
•
Manual mode;
Partial autonomous mode;
“Pilot-In-The-Loop” mode;
Fully autonomous mode;
Failure mode(s);
Fail-safe mode(s).
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WVU UAV & Payload Development Cycle (cont.)
Step-by-step Design/Selection Process of a (UAV+Avionic System)
-Specification of the Autopilot/Guidance/Navigation capabilities and desired level of
autonomous operations
-Selection of the “commercial” avionic package – OR – design/manufacturing of
“customized” avionic package satisfying the above specs.
-Selection of the “commercial” UAV platform – OR – design/manufacturing of
“customized” UAV platform.
NOTE : Any approach needs to provide optimal values for two parameters:
- T/W (Thrust/Weight ratio)
- W/S (Wing load)
- Initial flight testing program
- assessment of handling qualities
- assessment of data acquisition/avionic performance
- acquisition of PID flight data (for development of mathematical model)
- Development of
mathematical model from PID flight data
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WVU UAV & Payload Development Cycle (cont.)
Step-by-step Design/Selection Process of a (UAV+Avionic System) – cont.
- Development
of a Simulink flight
simulation environment – using
mathematical model developed
from PID data.
-For ‘customized’ avionics, design
of autopilot/guidance systems.
-Mission planning using simulator
with autopilot/guidance systems
-Final validation and verification of
operational capabilities through flight testing.
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WVU UAV & Payload Development Cycle
…. starting point !!
Mission Definition
Payload/Avionics Design
General (UAV) Design
Simulation Development
with Generic model
Hardware Selection
Control Law Design
Optimization:
-T/W
-W/S
- Landing & takeoff
performance
- Handling qualities
Fueslage Design
{
Software Architecture
Control Law Simulation
Wing Design
On-Board Software
Control Law Validation
Tail Design
Payload Assembly
Propulsion
Construction / Assembly
Initial Vehicle
Flight Testing
Payload Flight Testing
Parameter IDentification
(Mathematical Model)
Flight Test
Mission Objectives
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Videos
– UAV compilation video
– 2 Phastball video files
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Videos
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Overview of WVU
UAV Research Projects
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Main Emphasis of
WVU UAV Research Program
Addressing critical research needs in the emerging area of
UAV technology
- UAV formation flight;
- autonomous aerial refueling (AAR) for UAVs;
- fault tolerant flight control systems for UAVs;
- sensor fusion for UAVs.
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Recent WVU UAV Research Projects
- Intelligent Flight Control System (NASA F-15 and WVU YF-22) project
(sponsored by NASA Dryden)
- Autonomous Aerial Refueling (AAR) for UAVs project (sponsored by the
USAF).
- YF-22 ‘Formation Flight’ project (sponsored by AFOSR)
- Aviation Safety (sponsored by NASA Langley)
- NASA Space Robotics – not UAV related (sponsored by NASA Goddard)
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Intelligent Flight Control System
(NASA F-15 and WVU YF-22)
Sponsored by NASA Dryden
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Goals of the NASA Intelligent Flight Control System (IFCS) program
… To design, validate, and flight-test fault tolerant control laws based on the use of on-line
learning neural networks (“Gen_1” and “Gen_2” control laws with increased criticality of
the role of the NNs).
Other project members: NASA Dryden, ISR, Boeing, NASA Ames.
NOTE: Only general information about the NASA portion project are here provided due to
proprietary issues.
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‘Gen_1’ IFCS F-15 Program
(2001-2005)
Sensors
Baseline
Neural
Network
Online
Neural
Network
control
commands
pilot
inputs
baseline
derivative
derivative
estimate
derivative
correction
Controller
Real-Time
PID
derivative
error
+
General Approach
Real-Time PID for on-line estimate of selected stability & control derivatives (S&CD)
to update the aerodynamic look-up tables. Updated (S&CD) provided to the LQRbased control laws (SOFFT).
WVU Tasks within the ‘Gen_1’ IFCS F-15 Program (2001-2005)
•Development of ‘Gen_1’ IFCS F-15 modular and reconfigurable flight simulator (with graphic display
in AVDS or VRT – Simulink
•Participated in the “PID IFCS Research Group” for the preliminary analysis of several PID techniques.
•Development of a WVU PID Library with several PID methods for on-line/off-line applications.
•Development of real-time codes for two PID techniques:
Locally Weighted Regression (LWR) – based in the time domain;
Fourier Transform Regression (FTR) – based in the frequency domain.
•Simulation studies leading to the final selection of the LWR as the PID method for Gen-1 control laws.
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56/166
Library currently
available on
Mathworks web site.
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‘Gen_2’ IFCS F-15 Program (2003-2009)
General Approach
Non-Linear Dynamic Inversion (NLDI)-based control laws with neural augmentation
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• Development of ‘Gen_2’ IFCS F-15 modular and reconfigurable flight simulator (with graphic display
in AVDS or VRT – Simulink).
• Design and tuning of the VCAS control laws. Support for auto-coding for implementation on the
ARTS2 flight computer.
• Design of the integration of the VCAS control laws with the NN Augmentation. Performance
comparison with different NN algorithms (Sigma Pi from NASA Ames, SHL from Georgia Tech,
EMRAN from WVU)
• Development of a Safety Monitor (SM) scheme for the pilot to allow “safe” transition from “nominal
flight conditions” to “research nominal flight conditions” to “research surface failure flight conditions”.
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WVU “Gen_2” IFCS F-15 Simulator
AVDS Display of the WVU IFCS F-15 Sim
Main Components
•IFCS F-15 “open-loop” dynamics
•wind & turbulence model
•actuator dynamics
•control laws
•pilot interface
•graphic visual display
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WVU effort (2004-2009) - Flight Testing of the “Gen_2” Control Laws
using a WVU YF-22 research aircraft
… for safer and faster validation of the
Gen_2 IFCS control laws, with emphasis
on the neural augmentation of the NLDI scheme
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WVU YF-22 ‘Formation Flight’ project
Sponsored by Air Force Office of Scientific Research (AFOSR)
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Goal
From customer (Air Force Office of Scientific Research)
… to demonstrate GPS-based formation control using jet-
powered remotely controlled aircraft under maneuvered
flight.
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Tasks
Task #1 - Design and manufacturing of the WVU YF-22 research aircraft fleet given
mission specifications (formation flight)
Task #2 - Design and development of electronic payload necessary to achieve closedloop formation flight.
Task #3 - Determination of accurate math models from flight data (PID analysis) for:
- aircraft dynamics;
- actuator dynamics;
- engine response.
….
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Tasks (cont.)
Task #4 - Development of a modular flight simulator for closed-loop formation flight.
Task #5 - Design of formation flight control laws.
Task #6 - Validation of formation flight control laws via simulation study
Task #7 - Flight testing demonstration of formation control laws using:
> “virtual leader & physical follower” (1 aicraft)
> 2 aircraft formation
> 3 aircraft formation.
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Formation Flight Mission Design
- The aircraft are manually flown for take-off and landing.
- The formation is engaged at a tentative “meeting point” . From that moment, the
‘followers’ will automatically follow the ‘leader’ according to a pre-selected
formation geometry using their own GPS data and GPS data from the leader.
Data Links : GPS data
from ‘leader’ to ‘followers’
Leader : Manual flight
‘Followers’: Manual flight during
takeoff and landing
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Design and Manufacturing of the WVU YF-22 Models
Boundary Conditions
- Previous ‘smaller’ YF-22 design with very desirable handling qualities
- Compliance with FAA regulations for remotely controlled aircraft:
- total aircraft take-off weight :
< 55 lbs
- maximum altitude:
~500 ft
- aircraft in visual contact & under pilot control at all time
Step #1
Preliminary estimate of payload :
Preliminary estimate of aircraft structure:
Preliminary estimate of fuel weight:
Preliminary estimate of engine weight
~ [10-12] lbs
~ [20-25] lbs (without fuel)
~ 10 lbs
~ 10 lbs
Step #2
From prior design experience, determination of TWO critical design parameters:
T/W (Thrust/Weight ratio) ~ 0.5
W/S (Wing Load) ~ [55-65] oz/sqf -or- [3.5 - 4] lbs/sqf
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Step #3
Selection of jet engines.
Available jet sizes:
8.5 lbs.
28 lbs.
55 lbs.
90 lbs.
Maximum take-off weight (FAA compliance): 55 lbs.
Desirable T/W ~ 0.5
Selected engine: 28 lbs RAM 1000 jet
(June 2010 update: Jetcat 30 lbs jet)
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Step #4
Maximum TOW ~ 55 lbs
W/S
~ 4 lbs/sqf
Wing surface approx. 13 sqf
Selection of wing profile with appropriate curvature and introduction of flaps
to meet landing speed requirements ( < 60 mph)
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Step #5
Design of horizontal tail to satisfy 2 inch static margin
(from prior design experience)
Design of vertical tail to satisfy ground controllability during takeoff
(speed range ~ [40-50] mph).
Step #6
Design of fuselage. Enlargement from prior YF-22 design:
8 in. additional length
4 in. additional width
Estimate of the mass and inertial characteristics.
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Step #7
AUTOCAD drawings of fuselage.
Production of fuselage templates every 2 inch.
AUTOCAD drawing of wings / horizontal tail / vertical tail.
Step #8
Development of the pug-mold system for the YF-22 fuselage. Production of the
1st shell (used as iron-bird) followed by the manufacturing of 3 final
production shells.
Step #9
Installation of structural reinforcements and bulkheads for the fuselage.
Installation of landing gears.
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Step #10
Production of aerodynamic control surfaces. Customized installation of servos
and potentiometers (for measuring control surface deflections).
Step #11
Installation of engine, engine structural protection, and customized fuel tanks.
Step #12
Interface between wings and fuselage.
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Final Step ..priming & painting
Wing surface : approx. 13 ft2
Thrust (cruise conditions) : approx. 28 lbs
RAM 1000 jet. RPM range : 36,000 @ Idle - 126,000 Max
Weight (configuration #1) : 46 lbs
Configuration #1: with electronic payload, without fuel
Weight (configuration #2) : 54 lbs.
Configuration #2; with electronic payload, with full fuel
Wing span: approx. 6’
Length: approx. 8’ (with nose probe)
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Summary of Aircraft Characteristics (MKS units)
–Length:
~ 2.5 m
–Wing span:
~2m
–Wing area:
~1.3 m2
–Payload:
max ~5.5 Kg
–TOW:
avg ~22 Kg
–T/W ratio:
~0.56 (@ 22 Kg)
–W/S ratio:
~ 15.25 Kg/m2
Engine Specifications (RAM 1000)
– Maximum Thrust: ~12.7 Kg (later ~13.6 Kg)
– Fuel Consumption: ~0.35 Lt/min
– Maximum RPM: 126,000
– Mission length: max 12 minutes
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WVU YF-22 Models
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On-Board Sensors
Sensor
Signal
Unit
Range
Nose Probe
Alpha
Degree
±25
Beta
Degree
±25
Static Pressure
PSI
0-15
Dynamic Pressure
PSI
0-1
Pitch Angle
Degree
±60
Roll Angle
Degree
±90
Acceleration -X
g
±10
Acceleration -Y
g
±10
Acceleration -Z
g
±10
P
Degree/sec
±200
Q
Degree/sec
±200
R
Degree/sec
±200
Temperature
Sensor
Temperature
°F
30-120
Potentiometers
Surface deflections
Degree
±25
Vertical Gyro
IMU
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Development of Electronic Payload
CPU Module
Data Acquisition Module
Power Supply Module
Pressure Sensor
Servo Control Module
Compact Flash Card and Reader
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Air Data Probe
Development of Electronic Payload (cont.)
Inertial Measurement Unit (IMU)
Vertical Gyro
Potentiometer
Very
important
sensor !!
GPS Receiver
GPS Antenna
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Interface Board (Baseboard)
Interface Panels
Command
Signal
Controller
Receiver
High/low
Voltage
OBC
Channel selection
signal
switch
SIO
Commands
Command
Module
PWM
Commands
Controller
Board
Servos
Servos control
signal (PWM)
Controller Board Design
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Controller Board
Power Supply
Sensor Hub
Servo Hub
Battery Cell
R/C Servo
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Design of the On-Board Computer (cont.)
Controller
Board
Servo Control
Module
DAQ Card
Power Supply
Card
CPU Card
Interface-Board
CF Card
Reader
Computer Box
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Final Assembly of the Electronic Payload
GPS
Compact Flash
Vertical Gyro
OBC
IMU
Battery Pack
Sensor Cables
Power Supply
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Flight Testing Activities
The flight testing activities included the following phases:
Phase #1 Flights for assessment of handling qualities (for each aircraft);
- Maiden flight
- Evaluation of longitudinal and lateral-directional characteristics starting
with NO PAYLOAD configuration, with incremental additions
of 4 lbs of DUMMY PAYLOAD (up to 12 lbs).
- Assessment of ‘go around’ and aborted landing characteristics
- Detailed assessment of fuel consumption
- Evaluation of pilot fatigue
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1st Flight of 1st Aircraft
VIDEO – Segments of AC#1 maiden flight (‘Blue ship’)– evaluation of handling qualities
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Flight Testing Activities (cont.)
Phase #2
GPS communication flights;
- Air-to-ground communication of GPS data
- Air-to-air communication of GPS data
Phase #3
Data acquisition flights for Parameter Identification (PID) analysis;
- Longitudinal PID maneuvers
- Lateral-Directional PID maneuvers
Phase #4
Engine PID flights (for evaluation of throttle/airspeed response);
Phase #5
Flight testing for assessment (and tuning) of “Inner-loop” control laws
Phase #6
Flight testing for assessment (and tuning) of “Outer-loop” control laws
(along the forward, lateral and vertical channels).
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Flight Testing Activities (cont.)
Phase #7
Phase #8
Phase #9
‘Virtual leader’ flights (for risk-free validation of formation control laws)
Pilot training flights
- for “tentative meeting” prior to engaging formation;
- for “escape maneuvers” in case of too-close contact;
Final communication tests with data exchange between 2 aircraft;
Phase #10 2-aircraft formation flights;
Phase #11 3-aircraft formation flight (FINAL DEMONSTRATION)
A total of 187 flights have been conducted with the 3 aircraft for this project !
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PID Study – Development of Aircraft Mathematical Model
Several flights were performed to collect flight data for system identification
purposes. This process is known as (Parameter IDentification) PID study.
The following flight data were recorded for 77 PID maneuvers (8 flights):
• H,V (SenSym Pressure Sensors)
• Ax, Ay, Az, p, q, r (Xbow IMU)
• ,  (GoodRich Gyro)
•  , (SpaceAge Nose Probe)
• E, A, R (potentiometers).
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PID Study – Development of Aircraft Mathematical Model (cont.)
The Matlab Identification Toolbox (in particular the ‘n4sid’ method) was initially used to identify
the longitudinal and lateral mathematical model of the aircraft :
0
-0.1711  v   20.1681 
 v  -0.2835 -23.0959
   0
    0.5435 
-4.1172
0.7781
0
 
 
 iH
q  0
-33.8836 -3.5729
0   q  -39.0847 
  
  

0
1
0    
0
   0

Longitudinal
-0.7713 
    0.4299 0.0938 -1.0300 0.2366      0.2724
  
0   p  -101.8446 33.4738   A 
 p   -67.3341 -7.9485 5.6402

 r   20.5333 -0.6553 -1.9955
0   r   -6.2609 -24.3627   R 
  
  

0
1
0
0    
0
0
   

Lateral-Directional
8
Measured
Simulated
8
Measured
Simulated
6
6
4
Beta (deg)
Alpha (deg)
4
2
2
0
0
-2
-2
-4
554
554.5
555
555.5
Time(sec)
556
556.5
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526
527
528
529
Time(sec)
530
531
532
A non-linear mathematical model was later evaluated for developing a detailed ‘formation
flight” simulation environment. The model was obtained using Matlab-based non-linear
minimization algorithms.
x  f ( x,  , G, FA ( x,  ), M A ( x,  ))
y  g ( x,  , G, FA ( x,  ), M A ( x,  ))
with:
C D ( x ,  ) 
FA  qS  CY ( x,  ) 


 CL ( x,  ) 
For example:
 bCl ( x,  ) 
M A  qS cCm ( x,  ) 


 bCn ( x,  ) 
CL ( x,  )  CL0  CL  CLq q  CLiH iiH  ...
G includes the geometric and inertial characteristics of the UAV aircraft.
The product of inertia and the moments of inertia  I XZ , I XX , IYY , I ZZ  were evaluated
experimentally using a ‘pendulum’ apparatus.
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Geometric and Inertial Data (with a 60% fuel capacity)
c = 0.76 m, b = 1.96 m,
S = 1.37 m2
Ixx = 1.6073 Kg m2, Iyy = 7.51 Kg m2, Izz = 7.18 Kg m2, Ixz = -0.24 Kg m2
m = 20.64 Kg, T = 54.62 N
Longitudinal Aerodynamic Derivatives
cD 0 = 0.008, cD α = 0.507, cD q = 0, cD iH = -0.033
cL 0 = -0.049, cL α = 3.258, cL q = 0, cL iH = 0.189
cm 0 = 0.022, cm α = -0.473, cm q = -3.449, cm iH = -0.364
Lateral-directional Aerodynamic Derivatives
cY 0 = 0.016, cY  = 0.272, cY p = 1.215, cY r = -1.161, cY dA = 0.183, cY dR = -0.459
cl 0 = -0.001, cl  = -0.038, cl p = -0.213, cl r = 0.114, cl dA = -0.056, cl dR = 0.014
cn 0 = 0, cn  = 0.036, cn p = -0.151, cn r = -0.195, cn dA = -0.035, cn dR = -0.055
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Using experimental set-ups, the engine and actuator transfer functions were also developed
and validated using a simple BLS PID technique:
GT ( s) 
T ( s)  T0 ( s)
KT  d s

e
T ( s)
1 T s
KT  0.624
with:
 T  0.25sec  d  0.26sec
4
Measured Data
Simulated Data
3
Left Rudder (deg)
2
1
GAct ( s ) 
e  d s
1  as
1
0
with:
-1
 d  0.02sec
 a  0.0294  0.0424 sec
-2
-3
294
(depending on control surface)
294.5
295
295.5
296
296.5
Time(sec)
297
297.5
298
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Design of Formation Control Laws - Formation Geometry
WL
Leader Aircraft
v
VL
‘Formation control’ problem divided into:
-horizontal tracking (level plane)
- forward control channel
- lateral control channel
-vertical tracking
- vertical control channel
fc
Objective
x (North)
Follower Aircraft
Minimization of the following parameters
lc
hc
Desired follower
position
 l   sin   L   cos   L  0  xL  x   lc 
  
  

 f   cos   L  sin   L  0  yL  y    f c 
h   0
0
1  zL  z   hc 
  
sin   L  
y (East)
o
Earth -Fixed Reference
with:
cos   L  
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VLy
VLx2  VLy2
VLx
VLx2  VLy2
Design of Formation Control Laws - General Approach
WL
Leader Aircraft
v
VL
Design based on an Inner/Outer Loop
approach for each of the 3 channels:
- forward (along x)
- lateral (along y)
- directional (around z)
fc
x (North)
Follower Aircraft
lc
hc
Desired follower
position
y (East)
o
Earth -Fixed Reference
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Design of Formation Control Laws
Inner Loop (Roll, Yaw, Pitch Control)
- ‘lateral-directional’ controllers (for holding
d and heading)
 A ( s)  K p p( s)  K ( ( s)  d ( s))
s
 R ( s)  K r
r (s)
s  0
Proportional compensator
- ‘longitudinal’ controller (for holding
d )
i ( s)  Kq q( s)  K ( ( s)  d ( s))
H
d , d
PI compensator
provided by the OUTER LOOP control laws
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PI compensator
Inner Loop (Pitch Control)
Using MATLAB ‘Sisotool’ with arbitrary damping:
Root Locus
Open-Loop Bode Diagram
50
0
10
-50
-100
5
Short-period damping ratio: 0. 54
-150 G.M.: 15.9 dB
Freq: 11.5 rad/sec
Stable loop
-200
0
Kq  0.12, K  0.50
360
180
-5
0
-10
-180
P.M.: 87 deg
Freq: 1.49 rad/sec
-15
-10
-5
Real Axis
0
-360
-2
10
0
2
10
10
Frequency (rad/sec)
4
10
NOTE: 1 point flight condition
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Inner Loop (Roll Control)
Root Locus
Open-Loop Bode Diagram
50
0
main damping ratio: 0.35
10
-50
K p  0.04, K  0.35
-100
5
-150 G.M.: 13.4 dB
Freq: 11.8 rad/sec
Stable loop
-200
0
360
-5
180
0
-10
-180
P.M.: 78 deg
Freq: 2.63 rad/sec
-15
-10
-5
Real Axis
0
-360
-4
10
-2
0
2
10
10
10
Frequency (rad/sec)
4
10
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Inner Loop (Yaw Control)
Root Locus
Open-Loop Bode
50
40
30
G.M.: 20.3 dB
Freq: 27 rad/sec
0 Stable loop
Dutch-Roll damping ratio: 0.7
20
-50
10
-100
0
-150
Kr  0.16
540
-10
360
-20
180
-30
0
P.M.: 95.1 deg
Freq: 7.25 rad/sec
-10
-8
-6
-4
-2
Real Axis
0
-180
-4
10
-2
0
2
10
10
10
Frequency (rad/sec)
4
10
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Outer Loop
(For Holding Formation Geometry)
( f , l , h,    L )
The 3 distance errors and the difference between the velocity heading angles are evaluated
in real-time from the leader and follower position and velocity GPS measurements.
- ‘vertical’ controller
 d  K z h  K zs h
K z  3.23, K zs  1.76
PD compensator
using MATLAB ‘Sisotool’
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Outer Loop
(For Holding Formation Geometry)
- ‘horizontal’ controller (forward and lateral distance)
 T 
f f
   f (    L ,  l  ,  )
  l 
 d
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Dynamic Inversion (DI)
controller
Brief Review of “Dynamic Inversion”
Dynamic inversion is also known as “Feedback Linearization”. DI is a
technique mostly developed by Isidori and Byrnes featuring a non-linear
feedback to transform a nonlinear dynamical system into a linear one. Next,
the system can be controlled with conventional ‘linear’ control techniques.
Consider a generic non-linear system:
 x   f ( x)   g ( x) 
 y    h( x )    d ( x )  u
  
 

with xn , ym, um .
The main concept is to derive the output equation until the input ‘u’
appears explicitly into the equation.
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Outer Loop – Horizontal Formation Control
Using trigonometric transformations, for the horizontal plane :
 l   sin   L   cos   L   xL  x   lc 
 


 f   cos 
    L  sin   L    yL  y   f c 
sin   L  
VLy
V V
2
Lx
2
Ly
cos   L  
VLx
VLx2  VLy2
WL
VL
Leader Aircraft
For the vertical plane:
fc
h  H L  H F  hc
x (North)
Follower Aircraft
lc
hc
Desired follower
position
y (East)
o
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Earth-Fixed Reference
Outer Loop – Horizontal Formation Control (cont.)
   
The application of the DI approach involves the determination of:   ,  
f f
   Vxy sin     L  
f 


W

  
L 

V

V
cos





f
xy
L 
 
   Lxy
Vxy


V
cos(



)

sin(



)
L
1
L
  g tan   
   xy
V
d 
 V
 
 f   V sin(    )  Vxy  cos(    )   T  K  
T T 
L
1
L  b
 xy
V

Vxy
f 
  sin(    L ) 
cos(    L ) 
f 

2 
  W LVxy  sin(    )   W L     W L  
cos(



)
V
 

L 

L 
 
1 
1
cos  cos 
m
2 
qS
 cD cos   cY sin    g sin 
m
where:
Maggio 2013
Outer Loop – Horizontal Formation Control (cont.)
• Forward DI Control Law:
T 

m

KT cos  

d sin(    L )  f d cos(    L )  
1
KT
1

2
  0V S  CD 0  CD 0   m sin   Tb 
2

m
W L  cos(    L )  f sin(    L ) 
KT cos 
• Lateral DI Control Law:

1

g
cos


d  arctan 

d cos(    L )  f d sin(    L )  
V
WL 
W L   sin(    L )  f cos(    L ) 

g
g cos  
• The above control laws transform the horizontal kinematics into a series of
2 integrators. The resulting system leads to the design of a compensation- type
controller.
   d   K s  K
  
 f   f d    K fs f  K f


f
K  0.2027, K s  0.8894
K f  0.2419, K fs  2.0560
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FDC-based – Formation Flight Simulator (cont.)
VRT
(Virtual Reality)
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FDC-based – Formation Flight Simulator (cont.)
VIDEO – Random maneuver by the LEADER (wingman viewpoint)
Random maneuver by the LEADER aircraft
(flown by joystick)
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Control Laws Implementation – On-Board Schemes
FOLLOWER aircraft
LEADER aircraft
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Formation Flight Results – Virtual Leader (VL)
‘Virtual Leader’ approach : one aircraft (wingman) following a “virtual” leader
(pre-recorded aircraft track). VL flight testing results showed:
-) desirable tracking characteristics;
-) desirable matching between ‘real’ and ‘simulated’ flight data.
Tracking : Z Plot
Tracking : XY Plot
200
300
Virtual Leader
Follower
Simulated Follower
Desired Position
Virtual Leader position
at time t=243 sec
200
Virtual Leader
Follower
Simulated Follower
Desired Position
190
180
170
Real and Simulated Follower
positions at time t=243 sec
0
Virtual Leader position
at time t=269 sec
-100
z axis (m)
y axis (m)
100
160
150
140
130
-200
120
Real and Simulated Follower
positions at time t=269 sec
-300
-300
-200
-100
0
x axis (m)
100
200
300
110
100
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245
250
255
time (s)
260
265
Flight Testing : 3-Aircraft Formation
(~11 min. flight with 3 aircraft formation engaged for approx. 5 minutes)
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Flight Testing : 3-Aircraft Formation (cont.)
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Formation Control:
Vertical distance
3-Aircraft Formation Flight - Z Plot
350
Leader
Inside Follower
Outside Follower
Formation Control:
Forward and lateral distance
Z axis(m)
300
250
200
150
450
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500
550
Time(sec)
600
650
3-Aircraft Formation Flight - Vertical Distance Error
20
10
VD Error(m)
Tracking Error along
Vertical Channel
Inside Follower
Outside Follower
0
-10
-20
450
500
550
600
650
Lateral Distance Error
50
Lateral Channel
LD Error(m)
Inside Follower
Outside Follower
0
-50
450
500
550
600
650
Foward Distance Error
100
Forward Channel
FD Error(m)
Inside Follower
Outside Follower
50
0
-50
450
500
550
Time(sec)
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600
650
-Very desirable formation control on vertical channel for both “inside” and
“outside” followers.
-Margin of improvement in the formation control on forward channel for the
“outside” follower. NOTE: “outside” follower is required to accelerate (engine
throttle) during curve. Post flight analysis shows potential errors in the modeling
of the engine response (throttle setting / airspeed). Engine PID analysis was
performed at 1 throttle setting; additional engine PID maneuvers would have been
necessary!
-Margin of improvement in the formation control on lateral channel for the
“inside” follower. NOTE: “inside” follower is required higher bank angles during
turns. POTENTIAL SOLUTION: use of a 3rd order Dynamic Inversion in lieu of
 T 
the used 2nd order DI approach.
 T 
 

 
 d
 d
 aY 
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Videos
- 2 aircraft formation flight
- 3 aircraft formation flight
- Slides of aircraft and payload
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Summary and Lessons Learned
- .. A lot of work !
- Successful demonstration of 3-aircraft formation !
- Desirable performance of the GPS and RF-modem communication systems.
- Importance of the Virtual Leader (VL) concept as a safe approach for
Fine-tuning formation control laws without the risks of multiple flying aicraft.
- Importance of a flight simulation environment with detailed mathematical
models for:
- continuous refinement of the formation control laws;
- validation of the flight testing data;
- pilot training for rendezvous at “meeting point” prior to engaging
formation
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Summary and Lessons Learned (cont.)
-Due to FAA regulations and visual range constraint, ‘true’ steady state
performance of the formation control laws were never evaluated since each flight
was a sequence of ‘transients’.
-Although able to maintain formation geometry with reasonable accuracy,
margin of improvements were noticed for forward channel for “outside” follower
and “lateral” channel for “inside” follower.
-Importance of availability of experienced pilots and their willingness to train
and work with researchers.
-“Strange” events:
- hawk chasing a YF-22
- landing with a deer on the runway;
- take-off with sun / landing with snow!
- The FIRST 3-aircraft maneuvered formation flight with jet-powered UAVs.
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WVU Aviation Safety project
Sponsored by NASA Langley
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Objective:
1.
2.
Investigation of innovative approaches to help preventing a Loss of
Control (LOC) event;
Development of recovery methods leading to a safe landing
following LOC.
Research Areas:
Area #1: Improving aircraft situational awareness;
Area #2: Analysis of pilot response during aircraft upset conditions;
Area #3: Fault tolerant flight control for restoring aircraft handling
qualities;
Area #4: Motion planning and collision avoidance under dynamic
constraints;
Area #5: Achieve safe takeoff and landing.
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Project Overview
Area #2 - Pilot Response
Area #3 – Fault Tolerant Control
Area #1 – Situational Awareness
Area #4 - Motion Planning
“Healthy” Aircraft
(nominal conditions)
Sub-System Failure
Loss of Control (LOC)
Legend:
LOC Trajectory
LOC Prevention
Area #5 –Landing
LOC Recovery
Safe Landing
Loss of Aircraft
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Test Bed Aircraft (“Phastball”)
Design Objective:
A low cost test bed suitable for the high-risk
aviation safety research.
Design Features:
•
•
•
•
•
Composite construction;
Modularized aircraft components;
Electric propulsion system;
Thrust vectoring and differential thrust.
Low maintenance and short turn around time
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General “Phastball” Specifications
Length:
Wing span:
Wing Area:
TOW:
Wing loading
Payload capacity:
Thrust:
T/W ratio:
Battery:
Flight Duration:
Typical turnaround:
Cruise speed:
Control channel:
R/C system:
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~ 2.23 m (88 inch)
~ 2.23 m (88 inch)
~ 0.695 m2 (7.48 ft2)
~ 10.4 kg (23 lbs.)
~ 14.96 kg/ m2 (49.2 oz/ ft2)
~ 3.2 kg (7 lbs.)
2 × 25 N
~ 0.43
2 × 4900 mAh
~ 7 minutes
~ [20-25] minutes
~ 30 m/s
L/R elevators, L/R ailerons,
rudder, L/R engine, nose
gear, thrust vectoring
12-channel, 2.4 GHz,
4 antennas
…the “Constructor” and the “Destructor”
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Aircraft Development
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Avionic Systems for “Phastball”
Gen-V (Flight Control Research) for
BLUE and GREEN “Phastball”:
•
•
•
•
•
Reliable and flexible switching between
pilot and on-board control;
Dual R/C Receiver Configuration;
Support Matlab® Real-Time Workshop;
800MHz Processor;
Modest Size (6×5×3”) and Weight
(~3 lbs).
Miniature Data Logger (Data Collection)
for RED “Phastball”:
•
•
•
•
•
GPS/INS/Magnetometer;
4 PWM measurement channels;
8 A/D channel;
Expandable design;
Miniature size (2.8 × 1.8 ×1”) and
weight (~8 oz including GPS antenna
and battery ).
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Red “Phastball” – Data Acquisition Platform
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Video of Flight of Red “Phastball”
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Blue “Phastball” – Flight Control
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Video of Flight of Blue “Phastball”
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Green “Phastball” – Propulsion Assisted Control
GREEN aircraft = BLUE Aircraft Configuration + Longitudinal Thrust Vectoring
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Green “Phastball” – Flight Control + Thrust Vectoring
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Video of Flight of Green “Phastball”
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Video of Flight of Green “Phastball” : Thrust Vectoring
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On-Board Sensors for “Phastball”
ADIS16405 IMU
Goodrich VG34
3-axis Accelerometers (14-bit, ±10g’s)
±90°Roll ±60° Pitch Sensitivity
3-axis Rate Gyros (14-bit, +±150 deg/s)
16-bit analog, 10v Precision Reference
Erection Feedback w/i 0.25° of true
3-axis Magnetometers (14-bit, ± 2.5 mgauss)
SensorTechnics Pressure Sensors
Novatel OEMV1 GPS
Circular Error Probable 1.5m
20 Hz Update Rate
Static 800-1100 mbar
Dynamic 0-50 mbar
14-bit SPI Interface
Opti-Logic RS400 Laser Rangefinder
MP1545A Contactless
Potentiometer
400 yard 0.2 m resolution
10 Hz Update Rate
±0.5% linearity, ±50° electrical angle,
Max Operating Torque < 0.2 mNm
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WVU Concept of Flight Operations
Mode #1 - R/C and Autonomous
Mode #2 - Research Pilot In the Loop
Test Bed Aircraft
Test Bed Aircraft
Future
Optional
Link
Safety Pilot
Visitor(s)
On-Site
Pilot
Station
On-Site
Research
Station
JM Flight Testing Facilty
Safety Pilot
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Internet
Data
Remote
Pilot
Station
Remote
Research
Station
WVU Main Campus
WVU Ground Control Station
Weather and Command Information
Flight Displays
Spotter
Station
Phastball
Fuselage Mounting Rack
Computer
Rack
Pilot
Station
Storage
Research
Station
Flight Displays
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Front
Power
Distribution,
Toolboxes,
Storage
Cabin Access Door
Rear Access Door
WVU GCS Truck
WVU Ground Control Station (GCS) Configuration
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Development of the WVU Ground Control Station (GCS)
Purpose
Develop a robust and flexible Ground Control Station (GCS) software/system to
support all UAV research activities of the FCSL. WVU developed with the
support of a sub-contractor (WVHTF).
Goals
Fast reconfiguration for different research missions.
Loosely modeled after NASA Langley AirSTAR GCS.
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Hardware components of the WVU Ground Control Station (GCS)
GCS Computer (x2)
Video Encoder
ABMX Servers
2U Rack Mount
Short Depth
Core i5-2300
8GB DDR3 ECC
1.0TB SATA 3Gb/s (x2)
ATI Radeon HD4650
Windows 7 Pro 64-bit
Axis
240Q
Ethernet
RF Communications
Freewave
MM2 900/FGR-115RC
Serial/RS-232
Ground GPS
NovAtel
PowerPak-4
RS-232
Pilot Controls
USB
Weather Station
Peet Brothers
ULTIMETER 2100
RS-232
Ethernet Switch
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Cisco
SR2016T
16-port
Unmanaged
WVU Ground Control Station (GCS) Flow
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WVU GCS: Research Pilot Station
1. Synthetic Vision (X-Plane) with HUD
2. Primary Flight Display
- Shows attitude angels, α, β, air speed and
temperature, and IMU data.
3. Overhead Map Display
4. Weather display
- Shows wind direction, wind speed, barometric
pressure, and air/ground temperature.
5. Surfaces Display
- Shows graphically and numerically the surfaces
deflection for all the channels of the aircraft.
6. Live Feed of Aircraft Nose-Camera Video
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WVU GCS: Research Pilot Station (cont.)
Background Tasks
•
•
•
•
•
•
•
Multi-Function Displays
•
•
•
•
•
•
•
Weather
GPS
Downlink
Controls
PWM Reader
Uplink
Derived Data
NOTE: Background tasks start automatically
when the pilot station starts.
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Primary Flight Display
Map
Control Positions/Failures
Weather
Real-Time Strip Charts
Engineering Data
System Health/Status
WVU GCS: Engineering Station
Single Monitor
Able to view all
displays visible to
research pilot station.
Executes recorder
task in background.
Manages playback.
Not restricted to sideby-side MFDs. Can be
moved/resized.
Able to send non-control commands to aircraft.
For example: PID changes, waypoints, failures, etc …
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WVU GCS: Engineering Station (cont.)
Engineering Station capabilities
1.
2.
3.
4.
Manages 8 different Flight Modes
enabling a variety of flight or failure
conditions to be tested;
Sends eight different Control
Actions to the aircraft for activating
or deactivating different control
laws;
Switch the control of different
aircraft actuators between manual
and autonomous control;
Change three user-defined 16-bit
controller parameters.
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WVU GCS: Engineering Station (cont.)
Flight Data Display
Message Tree Window
Recorder Tool
Playback Tool
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WVU GCS: Observer Station
Access to all data received/sent by research pilot station and engineering station.
• Able to start and view all MFDs as independent windows.
• Not able to send any command to the aircraft.
Connection.
• Wired Network
• WiFi Bubble
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“Phastball” Aircraft Modeling Efforts
•
•
•
•
•
•
•
•
Aircraft 3D drawing (weight distribution, visualization, CFD);
Decoupled longitudinal and lateral-directional linear model (control law design);
Decoupled nonlinear aircraft model (flight simulation);
Coupled linear model with individual left/right control surface inputs (faulttolerant control law design);
Coupled nonlinear aircraft model (fault-tolerant flight simulation);
GPS Measurement
Engine model (control law design, flight simulation);
Actuator models (control law design/ flight simulation);
Stochastic sensor models (sensor fusion/ flight simulation).
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“Phastball” Platform for Sensor Fusion Research
Handling of Uncertainty, Nonlinearity, and Dimensionality
Nonlinear
Particle Filter (PF)
Unscented Kalman Filter (UKF)
Linear,
Extended Kalman Filter (EKF)
Gaussian, Kalman Filter(KF)
Gaussian Sum Filter
Low
Non-Gaussian
Dimensional
Unscented Information Filter (UIF)
Extended Information Filter (EIF)
Other Directions:
Stability, calibration,
fault-tolerance,
spatially distributed.
Attitude Estimation:
GPS + INS + Magnetometer
Altitude Estimation:
GPS + INS + Pressure
+ Laser Range Finder
+ Optical Flow
Wing Gust Estimation:
+ Vehicle Dynamics
+ Alpha, Beta
Information Filter(IF)
Navigation:
Multiple Layer,
Federated,…
High-Dimensional
+ Vehicle Dynamics
+ Landmark + …
Structural Mode, Failures…
Fusion of Multiple Sensory Data
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“Phastball” Platform for Flight Control Research
Multiple Skill Learning Control
Robustness
LQT baseline controller;
Robustness analysis.
Theory
e1
Supervisor
eN+1
2
3
u1
.
.
N
Adaptive Augmentation N
Forward
Model N
+
Baseline Controller N
_
+
uN
+
Forward
Model N+1
Adaptive Augmentation N+1
y
uN+1
+
_
Switching
Plant
Existing Skill Set
N+1
Adaptive augmentation
with MRAC, L1.
Stable switching system.
u
Trajectory Following
Application
1
Switching
Signal
Adaptation
Baseline Controller N+1
Learning of A New Skill
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Tracking a generated
trajectory for OBES and
close formation flight.
Control Augmentation
Designing with both F-15
and Phastball models.
‘Phastball’ Simulator
Mathematical Models
Nonlinear aircraft model;
Sensor/actuator models;
Engine models;
Failure emulation.
Flight Control
Outer-loop controller;
Inner-loop controller;
Adaptive augmentation.
3D Visualization
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Formation Flight with LQ Baseline Controller
 and  ref
10

 ref
5
XYZ: Trajectory Tracking
0
 [deg]
-5
200
-10
-15
-20
150
-30
0
20
40
60
50
80
100
time [s]
120
140
160
180
200
Surface Deflections
20
Aileron
Rudder
15
0
200
0
-200
Y [m]
-600
-400
-200
0
200
400
10
600
X [m]
Trajectory of the ‘Healthy’ Aircraft
Surface Deflection [deg]
Z [m]
-25
100
5
0
-5
-10
-15
-20
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0
20
40
60
80
100
time [s]
120
140
160
180
200
A Comparison of 3 Controllers (Healthy Aircraft)
LQ
LQ + MRAC
Differences with the Reference Models : Longitudinal Error
20
20
18
18
18
16
16
16
14
14
14
12
10
8
Longitudinal Error
20
Longitudinal Error
Longitudinal Error
LQ + L1
12
10
8
12
10
8
6
6
6
4
4
4
2
2
2
0
0
0
0
10
20
30
40
50
time [s]
60
70
80
90
100
0
20
40
60
80
100
120
time [s]
140
160
180
200
0
20
Performance Parameters of the Healthy Aircraft
Controller
Me (mean)
Ee (std. deviation)
LQ
0.58
5.31
LQ+MRAC
0.13
5.29
LQ+L1
0.16
5.26
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40
60
80
100
120
time [s]
140
160
180
200
Formation Flight with Elevator Failure
200
200
200
150
100
Z [m]
Z [m]
Z [m]
150
100
100
50
50
0
300
0
600
200
200
0
-200
Y [m]
-600
LQ
-400
-200
0
X [m]
200
400
600
400
100
200
0
0
-100
-200
-200
Y [m]
0
300
600
400
200
200
100
0
0
-300
-600
-400
-200
X [m]
LQ + MRAC
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-200
-100
-400
Y [m]
-300
-600
LQ + L1
X [m]
A Comparison of 3 Controllers (Elevator Failure)
LQ
LQ + MRAC
20
20
18
18
18
16
16
16
14
14
14
12
10
8
Longitudinal Error
20
Longitudinal Error
Longitudinal Error
LQ + L1
12
10
8
12
10
8
6
6
6
4
4
4
2
2
2
0
0
0
20
40
60
80
100
120
time [s]
140
160
180
200
0
0
20
40
60
80
100
120
time [s]
140
160
180
200
0
Performance Parameters of the Healthy Aircraft
Controller
Me (mean)
Ee (std. deviation)
LQ
2.77
5.44
LQ+MRAC
0.13
5.7
LQ+L1
0.11
5.03
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20
40
60
80
100
120
time [s]
140
160
180
200
Flight Testing Control Algorithms
•
•
A total of 9 flights performed.
Validated and tuned LQ-based inner-loop controller for both “Blue” and “Green”
aircraft.
0 deg. Roll Angle Tracking
2 deg. Pitch Angle Tracking
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Differential Thrust and Thrust Vectoring
•
•
•
A differential thrust of 12 N requires
about 5 deg. rudder deflection for
compensation at the tested flight
conditions.
The elevators were found to be
approximately 19 times more effective
in pitch control than vectored motors.
Simulation and flight testing results
showed good correlation.
Video
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Detection, Identification and Accommodation for Pitot Tube Failures
(in collaboration with Dr. Mario Luca Fravolini – Universita’ di Perugia)
Background Information
The analysis of the “Sensor Failure” has historically received much less attention than the
“Actuator Failure” problem due to the fact that triple or quadruple physical redundancy
- along with voting schemes – are typically used for sensors within flight control systems.
.. HOWEVER
…one class of sensor failure has recently received considerable attention due to a number of
aviation crashes, the most famous being the crash of Airbus 330-300 (AF 447: Rio de JaneiroParis, June 2009), that is “common failures” of all the Air Data System (ADS) / Pitot tubes.
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Detection, Identification and Accommodation for Pitot Tube Failures
Triple physical redundancy
for ADSs is typically used
on commercial jetliners.
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Recent crashes due to Pitot Tube failures
Air France Airbus A330-200, June 2009
(due to weather)
Aeroperu, Boeing B757, October 1996
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NASA Rockwell-MBB X-31, January 1995
(due to weather)
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Aeroperu, Boeing B757, October 1996
(due to improper maintenance – duct tape
on static holes prior to aircraft wash)
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…all the above crashes involved failures for ALL the onboard Air Data Systems (due to ice formation). … Additional
ADS would have NOT helped !
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Alternative Approach to the Problem:
Analytical Redundancy IN LIEU of Physical Redundancy
Analytical Redundancy Based Sensor Failure Accommodation (SFA)
Approaches to real-time state estimation of airspeed:
- Model-based estimation from conventional state estimation;
DRAWBACK: requiring full complete dynamic and
aerodynamic model;
- Model-free estimation
(NN-based, Non Linear KF-based, ..)
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Alternative Approach to the Problem:
Analytical Redundancy IN LIEU of Physical Redundancy
3 Pitot tubes
Air Data System (ADS).
Output: VT
Status: OK
Other
Aircraft
Onboard
Sensors
Aircraft
Dynamic/Aerodynamic
& Engine Models
Model-Free
Estimation
Output: VT
Airspeed
Sensor Failure
Detection &
Identification
Model-Based
State Estimation
Output: VT
ADS Failure ?
YES
ADS Status: Failed
VT from SFA
ADS Failure ?
NO
Aircraftdependent
Analytical Redundancy
Based Sensor Failure
Accommodation (SFA)
Gain
Scheduling
ADS Status: OK
VT from ADS
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Flight
Control Laws
Validation of the Analytical Redundancy using WVU YF-22 Flight Data
Complete set of data from 5 different flights, including:
- ALL control surfaces;
- Throttle settings;
- Airspeed data (from 3 simulated ADSs);
- ALL linear accelerations;
- ALL angular rates;
- ALL aerodynamic angles.
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Validation of the Analytical Redundancy using WVU YF-22 Flight Data
Phase #1 – Proof of concept
Approach #1 – Neural Network-based estimation of airspeed
Vs.
Approach #2 – Least Square-based estimation of airspeed
NOTE: A key drawback of Approach #2 is that its accuracy depends on the accuracy of the available
Dynamic/aerodynamic model WVU YF-22 aircraft (available from previous projects).
However, it provided a suitable benchmark for the performance of the NN-based method.
Phase #2 – Extended Study
Approach #1 – MLP Neural Network-based estimation of airspeed
Vs.
Approach #2 – EMRAN Neural Network-based estimation of airspeed
Vs.
Approach #3 – UKF-based estimation of airspeed
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Phase #1 - Validation of the Analytical Redundancy using WVU YF-22 Flight Data
Approach #1
NN-based
Estimation
Approach #2
Least Square
Based Estimation
Estimation
(Flt Data Set 003)
Validation
(Flt Data Set 005)
Mean (m/s)
Approach
Approach
#1
#2
-2.91e-05 -0.0437
STD (m/s)
Approach
Approach
#1
#2
0.5888
1.2071
-0.7490
0.7719
-0.7264
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1.3786
Phase #1 - Experimental Results using Additive “Failure Bias Unit” (FBU)
Failure Detection Time (sec)
1 Failure Bias Unit (FBU) for airspeed = 0.5 m/s
“Soft” failures:
longer detection time
Mag
1
5
10
15
20
25
30
35
40
“Hard” failures:
shorter detection time
Sudden Bias Failure (FBU = 0.5 m/s)
Pitot #1
Pitot #2
Pitot #3
A1
A2
A1
A2
A1
A2
18.6
17.36
36.16
35.82
22.44
22.3
12.28
11.18
7.64
5.4
12.78
11.26
8.84
7.82
4.90
3.6
6.06
4.34
6.72
5.70
3.86
2.82
3.9
2.70
5.12
4.36
3.08
2.26
2.82
2.08
4.42
3.60
2.62
1.96
2.36
1.70
3.7
3.06
2.26
1.72
2.04
1.42
3.14
2.64
1.98
1.56
1.68
1.22
CUSUM filter was used for detection (using on-line calculated
statistics of the residual of the error at fault free conditions).
Different Failure Detection filters could be used.
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Sensor Failure
Accommodation
Based on real-time
Least Square
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General Concept
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Type #1
Sudden Bias (SB) Failure
Type #2
Slow Ramp Bias (SRB) Failure
ADS Measured Airspeed with SRB Failure Injected at 150s
ADS Measured Airspeed with Failure Injected @ 150 s
45
46
Faulty Airspeed
Fault Free Airspeed
Faulty Airspeed
Fault Free Airspeed
44
42
Airspeed [m/s]
Airspeed [m/s]
40
39
38
37
36
35
40
38
34
35
144
3
146
148
150
152
154
156
2.5
36
33
158
2
1.5
1
0.5
34
0
-0.5
-1
140
30
140
150
160
170
Time [s]
180
190
200
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0
50
160
100
150
200
250
300
350
180
400
450
200
Time [s]
TFailure  100 s
220
240
260
Non Linear Kalman Filter: Unscented Kalman Filter (UKF)
States :
x  u v w   
Inputs :
u  ax
T
ay
p q r 
az
VT  u 2  v 2  w2
T
u  rv  qw  g sin   ax
v  pw  ru  g cos  sin   a y
w  qu  pv  g cos  cos   az
  p  q sin  tan   r cos  tan 
  q cos   r sin 
 ax   ax 
a   a 
 y  y
 az   az  IMU
Outputs :
y  
  
T


 q2  r 2
  pq  r    pr  q   x

 a
    pq  r 
p2  r 2
  qr  p    ya 


 
2
2
   pr  q    qr  p 
p  q   za 





 v  rx  pz 
 w  qx  py 
1
,


sin
 2 2


2
u


 u v w 
  VG ,   VG
  tan 1 
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Non Linear Kalman Filter: Unscented Kalman Filter (UKF)
UKF Airspeed Estimation Results, Flight 003
50
45
46
True V (Filtered)
Est. V (UKF)
True V (Filtered)
Est. V (UKF)
True V (Filtered)
Est. V (UKF)
44
45
40
42
40
40
V, m/s
V, m/s
38
V, m/s
35
35
30
36
34
30
32
25
30
25
28
20
350
400
450
500
550
600
Time, s
650
700
750
800
850
20
200
250
300
350
400
Time, s
450
500
550
600
48
True V (Filtered)
Est. V (UKF)
46
44
42
FDS 001
FDS 003
FDS 004
FDS 005
V, m/s
40
38
36
Mean(m/s)
0.0641
-0.1807
1.6308
-0.6875
34
32
30
28
200
250
300
350
400
450
Time, s
500
550
600
650
700
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Std (m/s)
2.0502
2.8930
1.7450
1.3947
26
200
250
300
350
400
450
Time, s
500
550
600
650
700
Neural Network: Multi-Layer Perceptron (MLP) Neural Network
Parameter
[Ni No]
[Num of Hidden Layer
[Training
Neurons] Goal]
[Max. Number of Epochs]
[Activation Function]
[Interconnection Weights]
Value
[10 1]
[5]
[0.002]
[500]
[log-sigmoidal (hidden layer)]
Initialized
using
Widrow-Nyugen Rule
linear(output
layer)]
Matlab NN Training Error Statistics (100 runs)
Mean, m/s
0.02
0
-0.02
-0.04
0
10
20
30
40
50
60
70
80
90
FDS
FDS
FDS
FDS
0.7
Std, m/s
100
0.6
0.5
0.4
0.3
0
10
20
30
40
50
60
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70
80
90
100
001
003
004
005
Neural Network: Multi-Layer Perceptron (MLP) Neural Network
MLP ANN Training Output - Flight Data Set 003
MLP ANN Validation Output - FDS 003 (Training), FDS 005 (Validation)
50
48
V Pitot(Filtered)
V Pitot(Filtered)
46
V AR(Training)
45
V AR(Training)
44
42
V, [m/s]
V, [m/s]
40
40
38
35
36
34
30
32
25
0
50
100
150
200
Time, [s]
250
Data Set
FDS 001
FDS
(tr)003
FDS
(tr)004
FDS
(tr)005
(tr)Set
Data
FDS 001
FDS
(tr)003
FDS
(tr)004
FDS
(tr)005
(tr)
300
350
30
400
FDS 001 (val)
Mean (m/s) Std. (m/s) Perf.(%)
1.6948e-005
0.4358
NA
((%)
-0.7268
0.7464
-103.7
1.8179
1.1164
-204.53
0.0875
0.8315
-115.92
FDS 004 (val)
Mean (m/s) Std. (m/s) Perf. (%)
-2.1743
0.9009
-106.7
-2.8208
0.9805
-167.6
1.8118e-005
0.3666
NA
-2.0833
0.8370
-117.35
0
50
100
150
200
250
Time, [s]
FDS 003 (val)
Mean (m/s) Std. (m/s)
Perf. (%)
0.6229
0.8351
-91.6
-6.4628e-005 0.3664
NA
2.0883
0.8094
-120.79
1.1513
1.8722
-386.16
FDS 005 (val)
Std.
Mean (m/s)
Perf. (%)
-0.1303
0.5708
-31
(m/s)
-0.6518
0.5550
-51.47
2.1990
0.7802
-112.82
-4.1971e-005 0.3851
NA
Maggio 2013
300
350
400
450
Neural Network: Extended Memory Resource Allocation Network (EMRAN)
EMRAN NN Training Phase, FDS {001, 003, 004, 005}
Value
120
[10 1]
[250 0.5 10 1]
[0.0001 0.0001 0.0001]
[1E-6 1E-6 1E-6]
[0.02]
[0.6 0.6 1]
[0 0.5]
110
[Ni, No]
[Nmax, Overlap, Radius, Prune]
[LR_weights, LR_sigma, LR_center]
[SF_weights, SF_sigma, SF_center]
[Err,_Thr]
[CD_Emax, CD_Emin, CD_Egam]
[FE_Thr, FE_Pole]
FDS
FDS
FDS
FDS
100
Number of Neurons
Parameter
90
80
70
60
50
40
0
No. of active neurons (at end of training)
Number of training epochs
Mean of training error (m/s)
Standard deviation of training error (m/s)
200
400
FDS 001
FDS 003
FDS 004
FDS 005
82
2000
-0.0149
0.4845
118
2000
0.0136
0.3620
79
2000
-0.0777
0.2775
116
2000
-0.0482
0.4664
Maggio 2013
600
800
1000 1200 1400
Epoch
1600 1800
2000
001
003
004
005
Neural Network: Extended Memory Resource Allocation Network (EMRAN)
EMRAN Training Output - Flight Data Set 003
EMRAN Training Validation - FDS 003 (Training), FDS 005 (Validation)
50
48
True V (Filtered)
Est. V (Training)
True V (Filtered)
Est. V (Validation)
46
45
44
42
40
V, m/s
V, m/s
40
35
38
36
34
30
32
30
25
200
250
300
350
400
Time, s
450
500
Data Set
FDS 001 (tr)
FDS 003 (tr)
FDS 004 (tr)
FDS 005 (tr)
Data Set
FDS 001 (tr)
FDS 003 (tr)
FDS 004 (tr)
FDS 005 (tr)
550
28
200
600
250
300
350
400
450
Time, s
FDS 001 (val)
FDS 003 (val)
Mean(m/s) Std(m/s) Perf. (%) Mean(m/s) Std(m/s)
Perf. (%)
1.9606
1.7830
-268
0.4845
NA
-0.0149
-0.3053
0.9415
-160
NA
0.0136
0.3620
2.0772
1.3457
-384.9
3.1129
2.0628
-643.35
0.1712
1.1098
-137.95
0.9886
1.5655
-235.65
FDS 004 (val)
FDS 005 (val)
Mean(m/s) Std(m/s) Perf. (%) Mean(m/s) Std(m/s)
Perf. (%)
-1.1329
0.7190
-48.4
1.3196
1.3744
-183.67
-2.2626
0.7056
-94.91
-0.1668
0.9519
-162.96
2.6669
1.5189
-447.35
0.2775
NA
-0.0777
-1.9170
0.6992
-49.91
-0.0482
0.4664
NA
Maggio 2013
500
550
600
650
700
Flight Data Set
FDS 001
FDS 003
FDS 004
FDS 005
MLP ANN
Mean
Std
-0.5599
0.7689
-1.3995
0.7618
2.0355
0.9015
-0.2800
1.1787
EMRAN NN
Mean
Std
0.7158
1.2921
-0.9116
0.8663
2.6190
1.6425
-0.2524
1.1248
Maggio 2013
UKF
Mean
0.0641
-0.1807
1.6308
-0.6875
Std
2.0502
2.8930
1.7450
1.3947
Thank You!
Maggio 2013

Development, Instrumentation, and Flight Testing of UAVs as

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