Upload
anonymous-ry7aem
View
35
Download
4
Tags:
Embed Size (px)
DESCRIPTION
fly vehicle identification
Citation preview
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/1Dr. Ravindra Jategaonkar
Examples of Flight Vehicle System Identification
DO-328: Proof of Match
X-31A: High Angle of Attack Modeling
C-160: Aerodynamic Data Base
Rotorcraft: High Bandwidth Models
Phoenix: RLV
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/2Dr. Ravindra Jategaonkar
This page is left intentionally blank.
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/3Dr. Ravindra Jategaonkar
General Concept of Aerodynamic Model Identification
Normal Flightregime
Landinggear
Ramp door
Groundeffect
Airdrop
High speedregime
Stall approachand stall
Take-offlanding
Singleengine
Groundhandling Engine
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/4Dr. Ravindra Jategaonkar
Typical Flight Test Program for System Identification (I)
Test Case: C-160 Transall
FL 300
FL 260
FL 160
FL 80
FL 20
100 150 200 250 300 True Airspeed (Kts)
0.2 0.3 0.4 0.5 Mach No.
Alt
itu
de
80 K
CAS
100
KCAS
120
KCAS
160 K
CAS
195 KCAS
277 KCAS230
KCAS
140 K
CAS
Elevator 3-2-1-1
Short Period
Elevator pulse
Phugoid
Bank angle
Level Turn Maneuver
Aileron/Spoiler
Bank to BankManeuver
Rudder Doublet
Dutch Roll
Thrust Doublet
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/5Dr. Ravindra Jategaonkar
Typical Flight Test Program for System Identification (II)Test Case: C-160 Transall
Noof Flights Test Purpose Flight
Hours
1
1
2
14
2
1
2
4
3
30
2
1,5
8,5
42,5
4,5
3
3,5
7,5
5
78
Check Flight Test Instrumentation
Envelope Expansion with Trailing Cone
Calibration of Airdata System
System Identification and Model Validation(4 Altitudes, 5 Speeds, 37 Configurations)
Identification of Ground Effects
22 Stall Maneuvers with 5 Configurations
Ground and Taxi Tests
Noise Recording in Hangar, on Runway and in Flight
Special Tests: Load Drop, Takeoff and Landing on Unprepared Runway and Runway with Snow
Flights with ~ 1000 Maneuvers and 37 configurations
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/6Dr. Ravindra Jategaonkar
C-160: High-Fidelity Simulator Data Base
Aerodynamic data base valid overthe entire operational envelope - Nonlinear aerodynamics - Interference and coupling effects
Identification of C-160 specificoperational characteristics - Ramp door interference, - air drop, etc.
Identification of dynamic stall - Unsteady flow separation
Identification of - Ground effect - Landing and Take-off - Failure states
Validation of flight estimated database - FAA Level-D
Flight estimate of dihedral effect
Point ID: Single trim pointsMulti-Point ID: Several trim conditions
-0.12
-0.18
-0.24-6 0 6 12deg
Point IdentificationMulti-point Identification
C lβ
η = 20°K
Angle of Attack
LandingFlap
η = 0°K
η = 30°K
η = 40°K
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/7Dr. Ravindra Jategaonkar
Identification of Elevator Control EffectivenessTest Case: Transall C-160
elevatordeflection
0 10 20 30time
s-18
2deg
Linear model
0
m/s2
deg/s
deg
Verticalacceleration
pitch rate
angle ofattack
-6
-128
-818
6
Accounting for nonlinearity
0
m/s2
deg/s
deg
Verticalacceleration
pitch rate
angle ofattack
-6
-128
18
6
Deflection
Effectiveness
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/8Dr. Ravindra Jategaonkar
Identification of Downwash Lag: Speed Brakes (1) Test Case: Transall C-160
Two-point aerodynamic model:Consider longitudinal motion:The lift and drag forces at the Wing and Tail are considered separately.
Knowing the forces the pitching moment can be computed readily:force times lever arm
In a general case,
[ ] eeLHLH
LLL CittCS
StCCCHW
δτααα δαε
αα ++−−++= ∂∂ )()()(0
[ ] VcqCCittC
SS
cl
CC WmqeeLHLHt
mm H 2)()(0 +++−−−= ∂
∂ δταα δαε
α
V/lt=τ Time required for the downwash to reach the Tail.
Other sources: Landing flaps; Direct-Lift-Control flaps; Wing mounted engines, and Speed brakes
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/9Dr. Ravindra Jategaonkar
Identification of Downwash Lag: Speed Brakes (2)
Test Case: Transall C-160
Flight tests: δf = 0°, 30°, 60°
Test procedure:Starting from horizontal level flight,apply 40% (100%) of speed brakes, holdfor some time, then retract the brakes.
Basic aerodynamic model augmentedwith incremental effects:ΔCLSB, ΔCDSB, ΔCmSB
For δf = 0°, speed brakes work primarilyin a classical sense as a drag inducingdevice.
Flight measured
Estimated
δf = 0°
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/10Dr. Ravindra Jategaonkar
Identification of Downwash Lag: Speed Brakes (3) Test Case: Transall C-160
For δf = 30° and 60°, time lag effectincreases proportionally
δf = 60°δf = 30°
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/11Dr. Ravindra Jategaonkar
Identification of Downwash Lag: Speed Brakes (4) Test Case: Transall C-160
Speed brakes consists of a lower and upperflaps on each wing;Deflected symmetrically.
Model augmentation:Downwash with transit time effect
Angle of attack at the tail:
are known (basic aerodynamic model)
Estimate of linearly dependent on flap deflection.
⎥⎦⎤
⎢⎣⎡ τ−α+τ−+τ−α−+α=α αδ∂
ε∂∂ε∂
αα∂ε∂ )t()t(C)t(i
SBS CSSCHH
SCand ∂ε∂
α∂ε∂
SBδ∂ε∂
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/12Dr. Ravindra Jategaonkar
Identification of Downwash Lag: Speed Brakes (5) Test Case: Transall C-160
Augmented aero model
incremental effects:
ΔCLSB, ΔCDSB, ΔCmSB
and
with transit time effect
adequately characterizes the Speed brake characteristics.
SBδ∂ε∂
δf = 60°δf = 30°
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/13Dr. Ravindra Jategaonkar
Modeling of Landing Gear Effects (1)Test Case: Transall C-160
Modeling and Experimental Aspects
Important for simulation of take-offs and landings
Longitudinal and lateral-directionalmaneuvers with gear down
8000 ft and 16000 ft120, 140 and 160 kts
Basic aerodynamic model: Discernible deviations in - longitudinal motion - lateral-directional motion variables
1.2
.010
0
-813
0-75
08
0
-80 20 40 60
Neglecting Landing Gear Effects
Time
m/s2
β
x
r
θ
α
a
deg/s
deg
deg
deg
Flight measurement (C-160 Transall)Model identification
s
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/14Dr. Ravindra Jategaonkar
Modeling of Landing Gear Effects (2)Test Case: Transall C-160
Modeling of Aerodynamic Effects due to LG
Incremental aerodynamic modeling
Longitudinal motion:Lift, drag and pitching moment coeff.ΔCLLG , ΔCDLG , ΔCmLG
Lateral-Directional motion:- Increased weathercock stability ΔCnβLG
- Sideforce due to sideslip ΔCYβLG
Accounting for Landing Gear Effects
Flight measurement (C-160 Transall)Model identification
0 20 40 60Time
β
x
r
θ
α
a
1.2
.010
0
-813
0-75
08
0
-8
deg
deg
deg
m/s2
deg/s
s
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/15Dr. Ravindra Jategaonkar
Vertical-acceleration
Pitchrate
Pitchattitude
Pitchacceleration
CG-location
-8
-135
-5
8
-28
-16
50
20
-8
-135
-5
6
28
-16
50
20
m/s2m/s2
deg/s deg/s
degdeg
2deg/s 2deg/s
% %
0 5 10 15Time (sec) 0 5 10 15
Neglecing Variations in Aircraft MassCharacteristics
Accounting for Variations in theAircraft Mass Characteristics
MeasurementSimulation
Time (sec)
C-160: Load Drop (4.6 t)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/16Dr. Ravindra Jategaonkar
This page is left intentionally blank.
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/17Dr. Ravindra Jategaonkar
Do-328: Stand-alone versus Integrated Models
ReversibleFlight Control
Dynamics
Aircraft MotionVariables
Pilot InputForces
Control SurfaceDeflect.
Aircraft MotionVariablesRigid Body
Dynamics
Flight controls stand-alone
ReversibleFlight Control
Dynamics
Rigid BodyDynamics
Integrated model
Control SurfaceDeflect.
Aircraft MotionVariables
Pilot InputForces
Control SurfaceDeflect.
Rigid-body stand-alone
measured data simulated data
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/18Dr. Ravindra Jategaonkar
Validation Example 1: Steady Sideslip (DO 328)Rigid-body Stand-alone Integrated Model: End-to-end Match
12
-1218
-184
-42
-220
-20
deg
deg
deg/s
deg/s
deg
β
φ
p
r
δr
0 25 50time s
β
φ
p
δr
12
-1218
-184
-42
-220
-20
deg
deg
deg/s
deg/s
deg
r
0 25 50time s
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/19Dr. Ravindra Jategaonkar
High activity task:
Single maneuver:
Tolerances: Ground effect:
Wind:
No closed-loop controller
Landing characteristics reproduced in thedatabase conform to the airplane.Complete sequence as a single time segment(starting from 200 ft AGL, flare, touch down,derotation, and initial ground roll)FAA: Landing phase may be split into two(approach and derotation after touch down)
3 kt on airspeed, 10 ft on altitude1.5 deg on pitch attitude, 2.0 deg on bankAnalytical function (tanh-approximation),Continuous transition from in-flight regime toon-ground
Flight card noted windWind components as the difference betweenmeasured tracking speed provided by IRS andthe true airspeed transformed in earth-fixedcoordinate system.
Validation Example 2: Normal Landing (DO 328)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/20Dr. Ravindra Jategaonkar
Normal Landing
Flight measured (DO 328)Model identifiedFAA AC 120-40C tolerances
δe
Expanded View (Touch down)
θ
φ
14 16
-5
52
80
60
0
-10
ft
deg
deg
deg
h
Time (sec)
1.5 deg
2 deg
10 ft
18
130
0 10 20-5
10-10
20-10
5-5
100
25090
kt
ft
deg
deg
deg
deg
V
h
θ
φ
δe
δr
1.5 deg
10 ft
3 kt
2 deg
Complete Landing Phase
Time (sec)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/21Dr. Ravindra Jategaonkar
Engine failure during the critical phase of take-off
Response to rudder and aileron important
Complete sequence as a single timesegment (stand-still, acceleration, rotation,and climb to 200 ft)
No closed-loop controller
Tolerances: 3 kt on airspeed20 ft on altitude
1.5 deg on pitch attitude2.0 deg on bank
Validation Example 3: Critical Engine Failure (DO 328)
Flight measuredModel identified
kt
ft
deg
deg
deg
deg
150
-500
300
-1000
15
-50
-5
0
10
30
-15
0
10
-20
0
0
-2000
4000daN
0 10 20 30 stime
left engine shut off
V
h
θ
φ
δe
δr
FL , FR
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/22Dr. Ravindra Jategaonkar
This page is left intentionally blank.
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/23Dr. Ravindra Jategaonkar
Experimental Aircraft X-31
Thrust vector control
What is X-31?- First international „X-System“- Technology Demonstrator
Goals- Enhanced Maneuverability (up to 70°)- Tactical advantages through Flight
beyond the limits
New Technologies- Thrust vector control- Aerodynamic design - Integrated Flight control system
Tasks- Flight envelope expansion- Development of Data base for
Simulation and FCS design- Handling qualities evaluation
Folie Nr.23
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/24Dr. Ravindra Jategaonkar
Control Surfaces of the X-31A
Thrust VectoringVanes (3)• -60° bis +35°• 60°/sec
Thrust VectoringVanes (3)• -60° bis +35°• 60°/sec
Inlet• 0° to -26°• 20°/sec
Inlet• 0° to -26°• 20°/sec
Trailing Edge Flaps (4) • ±30°• 80°/sec
Trailing Edge Flaps (4) • ±30°• 80°/sec
Canard (2)• -70° to +20°• 60°/sec
Canard (2)• -70° to +20°• 60°/sec
Leading Edge Flaps (2) • 0° to -40°• 25°/sec
Leading Edge Flaps (2) • 0° to -40°• 25°/sec
Speed Brakes (2) • 0° to +46°• 20°/sec
Speed Brakes (2) • 0° to +46°• 20°/sec
Rudder• ±30°• 80°/sec
Rudder• ±30°• 80°/sec
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/25Dr. Ravindra Jategaonkar
Flight Test Program
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Mac
h nu
mbe
r
Angle of Attack, deg0 10 20 30 40 50 60 70 80-10
Longitudinal motion
High Angle of AttackSupersonicLanding configurationData conditioningSeperate Surface Excitation
Longitudinal motion
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/26Dr. Ravindra Jategaonkar
Challenges of Flight in Post-Stall Regime
Basic aircraft is highly unstable;Resulting from aerodynamic design.
Motion variables and controls are highly correlated;Resulting from integrated flight control system.
Controller Suppresses the oscillatory and transient motion;Reduces drastically the information content.
Post-Stall Maneuver with large amplitudes;Conventional model structures inappropriate for analysis.
Determination of Air-Flow variables extremely critical; However, necessary for Stabilization of Flight systems.
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/27Dr. Ravindra Jategaonkar
Identification of controlled unstable aircraft
Problems- Increased Modeling effort for closed loop system- Numerical Problems in identification of open loop plant- Feedback of measured variables introduce input noise (stochastic system)
Parameter estimation methods- Equation error method with optional Data-Partitioning- Filter error method for problems with Measurement and Process noise- Extended Kalman filter
ControllerController UnstableAircraft
UnstableAircraft
Identificationin open loopIdentificationin open loop
Pilot
MeasuredControl inputs
MeasuredResponse
Noise
u z
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/28Dr. Ravindra Jategaonkar
AerodynamicControl surfaces
Pilotinputs
Thrust vector
Sensors
FCCFlight Control
Computer
FCC RedundancyManagement Signals
FTBFlutter test box
X-31Aircraft dynamic
Control commands
States
Realization of Separate Surface excitation (1)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/29Dr. Ravindra Jategaonkar
Realization of Separate Surface excitation (2)
AerodynamicControl surfaces
Pilotinputs
Thrust vector
Sensors
FCCFlight Control
Computer
FCC RedundancyManagement Signals
FTBFlutter test box
X-31Aircraft dynamic
Control commands
States
PilotTrigger Signal generatorSignal generator
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/30Dr. Ravindra Jategaonkar
Correlated Inputs (1)
X-31A: Pilot Input and Separate Surface Excitation α σδTEδcan
deg
deg
deg
deg
20
35
0
2
-15
-4520
-20
time s200 10 15 255
PID command
pitch command
angle of attack
canard
sym. trailing edge flaps
TV deflection in pitch
2.5
-2.5
deg20
-20
deg
pilot inputpitch doublet
canard 3211
elevator 3211
separate surface excitation (SSE)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/31Dr. Ravindra Jategaonkar
20 40 60 80Angle of Attack, deg
0.6
0.4
0.2
0
-0.220 40 60 80
Angle of Attack, deg
Pilot Input Separate Surface Excitation
Single maneuverData partitioningWindtunnel predicted
EstimatedWindtunnel
Cmδcan
X-31A: Canard Effectiveness
α σδTEδcan
Correlated Inputs (2)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/32Dr. Ravindra Jategaonkar
correlated state andcontrol variables
Derivatives that cannot be identifiedindependently from pilot input maneuvers
α, δC , δe and σ,
δa , δr and as well as p and rCm α , Cm , Cm δe and Cm σ ,Cn δa , Cn δr and Cnκ Clp and Clrκ as well as
δC
• relatively large standard deviations and uncertainties
• use fixed values from the X-31 database for some parameters
• combined roll/yaw-damping
with
ppr
lrClpCplpC ⋅⎟⎟⎠
⎞⎜⎜⎝
⎛⋅+=⋅*
αtan=pr
for the velocity vector roll0 5 stime
angle ofsideslip
deg5
-5
rollcommand
1
-1
deg
TV-deflectionin yaw -20
20diff. trailingedge defl.
deg/s
yaw rate
roll rate
-20
20
Roll Doublet
PID with Pilot Input -- Single Maneuver (1)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/33Dr. Ravindra Jategaonkar
PID with Pilot Input (2)Bank-to-bank maneuver at 54° angle of attack: LS and Filter error methods
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/34Dr. Ravindra Jategaonkar
PID with Pilot Input (3)Estimates of aerodynamic derivatives: LS and Filter error methods
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/35Dr. Ravindra Jategaonkar
X-31 Database Update (1)
-0.4
-0.3
-0.2
-0.1
0
0 20 40 60 80
Pilot Input
original data setsingle maneuverdata partitioningPID2 update
angle of attack, deg
C lβ
1/rad
0 20 40 60 80
Single Surface Excitation (SSE)
original data setflutter test boxPID2 update
angle of attack, deg
Dihedral Effect
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/36Dr. Ravindra Jategaonkar
X-31 Database Update (2)
Directional Stability
! " " #$
"
#
%
&
& ' ' ( )
"
! " " #$
"
#
%
&
& ' ' ( )
"
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/37Dr. Ravindra Jategaonkar
ReferenceMission:
Flight phases upon release:1) Acquisition2) Approach3) Flare4) Alignment5) Derotation6) Rollout
Launch at40m/s EAS
flare
118m/s
runway
acquisition dive
altitude
RLVapproachpath -23o
510m
2.65km6.6km
touch down71m/s
Towed to establish initial conditions
Phoenix freeflight profile
45m x 2100m
release Phoenixfrom helicopter
touchdown
lift-off
ground track
Launch at40m/s EAS
flare
118m/s
runway
acquisition dive
altitude
RLVapproachpath -23o
510m
2.65km6.6km
touch down71m/s
Towed to establish initial conditions
Phoenix freeflight profile
45m x 2100m
release Phoenixfrom helicopter
touchdown
lift-off
ground track
Phoenix: Reusable orbital glider (1)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/38Dr. Ravindra Jategaonkar
Overview and goals
Phoenix:Flight test vehicle developed and tested within German ASTRA program
Experimental steps towards the development of next generation space transportation systems
Primary Objectives:To demonstrate un-powered automatic landing of RLV configuration
Secondary Objectives:To generate flight validated database and representativemodels (vehicle + subsystems) as developmental tool for future applications
Phoenix: Reusable orbital glider (2)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/39Dr. Ravindra Jategaonkar
Phoenix Configuration
Typical characteristics of RLV configurations (Phoenix):Low L/D -- causes steep approach path (= 5.5)Low achievable CL -- high landing velocity (=71 m/s)Small wing span -- high roll sensitivityAft CG position -- statically unstable configuration
(time to double < 0.5 s)
Overall length = 7.8 m (including noseboom)Span = 3.84 mOverall height = 2.56 m (retracted landing gear)Mass = 1200 kgCG position = 70 % of bare fuselage length
Phoenix: Reusable orbital glider (3)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/40Dr. Ravindra Jategaonkar
Aerodynamic Database
Wind-tunnel tests:DNW German-Dutch wind tunnel
Test program:171 quasi-static polar curves,25 dynamic polar with
rapid control deflections29 polar curves in ground effect
Scaled models and full scale flight vehicle
Phoenix: Reusable orbital glider (4)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/41Dr. Ravindra Jategaonkar
Wind-tunnel testing in August 2003
Pre-flight checks: April 2004
calibration of flow angles:
α-error nonlinear: quadratic or piecewise linear
Accuracy:AoA and AoS: < 0.5°Horizontal velocity: 0.5 m/s
Phoenix: Reusable orbital glider (5)
-10 -5 0 5 10 15 20 25-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
alpha
Alph
a-er
ror
afte
rlin
ear c
alib
ratio
n
40 m/s70 m/s100 m/s
deg
deg
offsetc
d
c
dqp
korrqK
pα++=α β
βα
α
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/42Dr. Ravindra Jategaonkar
Free flights:Maiden flight on 8-May-2004Repeat flight on 13-May-20043rd flight with Offset on 16-May-2004
Configuration:Delta Wing, relatively low wing span3 controls (flaperons and rudder)Body flap and speed brake1200 Kg7m long3,48 m span
Highly dynamic behaviorHigh bandwidth control loops
Video free flight 1:
Phoenix: Reusable orbital glider (6)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/43Dr. Ravindra Jategaonkar
Aerodynamic DatabaseVerification and Update -- Principle
Phoenix: Reusable orbital glider (7)
MeasuredAX, AY, AZ,p, q, r,pdyn
p_dot, q_dot, r_dot
AX_cg, AY_cg, AZ_cg
X, Y, Z, L_cg, M_cg, N_cg
X, Y, Z, L_ac, M_ac, N_ac
FlightCX, CY, CZCLX, CMY, CNZ
Windtunnel Database(…V31.txt)
Measureddero, delo,dari, deli,dr, dbf, dsb,p, q, r,al, be
WT-PredictionCX, CY, CZCLX, CMY, CNZ
Δs
SysID
Corrections
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/44Dr. Ravindra Jategaonkar
Flight derived and WT predicted aero coefficients
-0.06
-0.03
0
0.03ZA03 ZA06 ZA05
CXCX
-0.03
-0.015
0
0.015
0.03ZA03 ZA06 ZA05
CYCY
-0.5
-0.25
0ZA03 ZA06 ZA05
CZCZ
-4
-2
0
2
4x 10
-3
ZA03 ZA06 ZA05
CLXCLX
-12
-6
0
2x 10
-3
ZA03 ZA06 ZA05
CMYCMY
0 50 100 150-4
-2
0
2
4x 10
-3
t in s
ZA03 ZA06 ZA05
CNZCNZ
-0.02
-0.01
0
0.01ZA03 ZA06 ZA05
dCX
-0.02
-0.01
0
0.01
0.02ZA03 ZA06 ZA05
dCY
-0.1
-0.05
0
0.05
0.1ZA03 ZA06 ZA05
dCZ
-2
-1
0
1
2x 10
-3
ZA03 ZA06 ZA05
dCLX
-4
-2
0
2
4x 10
-3
ZA03 ZA06 ZA05
dCMY
0 50 100 150-2
-1
0
1
2x 10
-3
t in s
ZA03 ZA06 ZA05
dCNZ
Phoenix: Reusable orbital glider (8)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/45Dr. Ravindra Jategaonkar
0 0.05 0.1 0.15 0.2 0.25-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
Angle of attack (rad)
Tota
l CX
ff-n1 flight measurementsff-n2 flight measurementsff-n3 flight measurementsff-n1 wind-tunnelff-n2 wind-tunnelff-n3 wind-tunnel
Flight derived and WT predicted longitudinal force coefficient
Flight data
Pre-flight ADB
Phoenix: Reusable orbital glider (9)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/46Dr. Ravindra Jategaonkar
0 0.05 0.1 0.15 0.2 0.25-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
Angle of attack (rad)
Tota
l CZ
ff-n1 flight measurementsff-n2 flight measurementsff-n3 flight measurementsff-n1 wind-tunnelff-n2 wind-tunnelff-n3 wind-tunnel
Flight derived and WT predicted vertical force coefficient
Rough order of discrepancies:CZ: 9-10%
Cm: < 3%
CD: ~10% Nonlinear
Pre-flight ADB
Flight-derived
Phoenix: Reusable orbital glider (10)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/47Dr. Ravindra Jategaonkar
sbsbref
q CXVL
qCXCXCXCX δα δα +++=Δ 0
bfbfref
q CZVL
qCZCZCZCZ δα δα +++=Δ 0
sbsbee CMCMCMCMCMY δδα δδα +++=Δ 0
12 Parameters CZ(), CX() and Cm() are estimated to reduce the deviations between flight measurements and WT-predictions.
Aero model update (In-Air)
Phoenix: Reusable orbital glider (11)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/48Dr. Ravindra Jategaonkar
Flight derived and updated database aero coefficients
-0.06
-0.03
0
0.03ZA03 ZA06 ZA05
CXCX
-0.03
-0.015
0
0.015
0.03ZA03 ZA06 ZA05
CYCY
-0.5
-0.25
0ZA03 ZA06 ZA05
CZCZ
-4
-2
0
2
4x 10
-3
ZA03 ZA06 ZA05
CLXCLX
-12
-6
0
2x 10
-3
ZA03 ZA06 ZA05
CMYCMY
0 50 100 150-4
-2
0
2
4x 10
-3
ZA03 ZA06 ZA05
CNZCNZ
-0.02
-0.01
0
0.01ZA03 ZA06 ZA05
dCX
-0.02
-0.01
0
0.01
0.02ZA03 ZA06 ZA05
dCY
-0.1
-0.05
0
0.05
0.1ZA03 ZA06 ZA05
dCZ
-2
-1
0
1
2x 10
-3
ZA03 ZA06 ZA05
dCLX
-4
-2
0
2
4x 10
-3
ZA03 ZA06 ZA05
dCMY
0 50 100 150-2
-1
0
1
2x 10
-3
t in s
ZA03 ZA06 ZA05
dCNZ
Phoenix: Reusable orbital glider (12)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/49Dr. Ravindra Jategaonkar
0 0.05 0.1 0.15 0.2 0.25-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
Angle of attack (rad)
Tota
l CZ
ff-n1 flight measurementsff-n2 flight measurementsff-n3 flight measurementsff-n1 wind-tunnel + Updatesff-n2 wind-tunnel + Updatesff-n3 wind-tunnel + Updates
Flight derived and Updated database vertical force coefficient
Phoenix: Reusable orbital glider (13)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/50Dr. Ravindra Jategaonkar
Delta CZ versus AoAwithout and with update
Important Inferences:- lift generated in flight is higher - component due to pitch rate in lift and drag is not adequately accounted for. - basic longitudinal force coefficient for clean configuration underestimated,- impact of speedbrakes overestimated.
0 0.05 0.1 0.15 0.2 0.25-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
Angle of attack (rad)
Del
ta C
Z
ff-n1ff-n2ff-n3
0 0.05 0.1 0.15 0.2 0.25-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
Angle of attack (rad)
Del
ta C
Z
ff-n1ff-n2ff-n3
Flight derived and Updated database (Continued)Phoenix: Reusable orbital glider (14)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/51Dr. Ravindra Jategaonkar
Flight derived and WT predicted In-Air + Landing Gear
0 0.05 0.1 0.15 0.2 0.25-0.015
-0.01
-0.005
0
0.005
0.01
Angle of attack (rad)
Del
ta C
X
ff-n1ff-n2ff-n3In-Air
Land
ing
Gea
r
Phoenix: Reusable orbital glider (15)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/52Dr. Ravindra Jategaonkar
LGLGsbbsref
q CXCXVL
qCXCXCXCX δαδα αδα ++++=Δ 0
LGLGbffbref
q CZCZVL
qCZCZCZCZ δαδα αδα ++++=Δ 0
LGLGsbbsee CMCMCMCMCMCMY δαδδα αδδα ++++=Δ 0
Aero model update: In-Air + Landing Gear-Effects)
In-Air updates fixed from initial 50 s maneuver
Phoenix: Reusable orbital glider (16)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/53Dr. Ravindra Jategaonkar
Delta CX versus AoAwithout and with update
Landing GearAero effect
Aero model update (In-Air + Landing Gear-Effects)
0 0.05 0.1 0.15 0.2 0.25-0.015
-0.01
-0.005
0
0.005
0.01
Del
ta C
X
ff-n1ff-n2ff-n3
0 0.05 0.1 0.15 0.2 0.25-0.015
-0.01
-0.005
0
0.005
0.01
Angle of attack (rad)
Del
ta C
X
ff-n1ff-n2ff-n3
Phoenix: Reusable orbital glider (17)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/54Dr. Ravindra Jategaonkar
This page is left intentionally blank.
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/55Dr. Ravindra Jategaonkar
Rotorcraft Modeling (1)
Integrated Approach to Rotorcraft Modeling and SimulationSID Models SIM & SID Models SIM Models
Classical SID approachDerivative modelsLinear/NL aerodynamicExtensive flight datafor point-model IDand validationStability & Controlanalysis andcontrol system design
Advanced integrated approachGeneric model Augmented with parametric submodelsNonlinear aerodynamicFlight data for sub-model ID and globalmodel validation
Classical SIM approachGeneric models based onmodular elements Nonlinear aerodynamicFlight data only formodel validationSimulation, performanceAnd vehicle design
System Identification System SimulationSystem Simulation &Identification
High Fidelity SimulationState Space Models
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/56Dr. Ravindra Jategaonkar
Rotorcraft Modeling (2)
SID: based on classical 6 DOF motionrigid-body, small excursions: linear modellarge excursions NL derivatives
States:
Inputs:
Outputs:
Example: EC-135,60 kts forward speed
Trqpwvux ][ θφ=
Tcolpedlatlonu ][ δδδδ=
Tzyx rqprqpwvuaaay ][ &&&θφ=
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/57Dr. Ravindra Jategaonkar
Rotorcraft Modeling (3)
SID: based on classical 6 DOF motionExample: EC-135, 60 kts forward speed
- Gauss-Newton method- concatenate several dynamic multistep inputs as well as frequency
sweeps with longitudinal, lateral, collective, and pedal inputs
1st and 2nd row: linear derivatives3rd and 4th row: a and b dependenciesweathercock stability parameter Cnβ (NV) for +/-ve side slipping
lonlonlonlon
pedpedpedpedrv
colcolpedpedlatlatlonlon
rqpwvu
NN
NNrNvN
NNNN
rNqNpNwNvNuNNN
δβδα
δβδααα
δδδδ
βδαδ
βδαδαα
δδδδ
++
++++
++++
++++++= 0~
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/58Dr. Ravindra Jategaonkar
Rotorcraft Modeling (4)
SID: classical 6 DOF motionExample: EC-135, 60 kts forward speed
Model predictive capability:- Pedal and lateral inputs- low-frequency models- Flying qualities investigations
time
rad/s0.3
-0.5
0p
rad/s0.3
-0.3
0q
rad0.2
-0.2
0α
rad0.3
-0.3
β 0
0 10 20 30 40s
75
35
%δlat,δped
rad/s0.3
-0.3
0r
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/59Dr. Ravindra Jategaonkar
ModelInversion
Analysisof FlightTest Data
SystemIdentification
Model ofHelicopterDynamics
ModelValidation
HelicopterResponse
RequiredResponse
ControlLaw
Pilot CommandModel
ActualHelicopterDynamics
ControllerInputModel of
HelicopterDynamics
-1
Feed-forward
Response Error
PI-Controller
-+
Feed-back
High bandwidth models:In-flight simulation: Explicit Model Following Control Design:Based on feed forward regulation, for more accurate mode control
Rotorcraft Modeling (5)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/60Dr. Ravindra Jategaonkar
Rotorcraft Modeling (6)High bandwidth models:
Extension of 6DOF model through additional time delay:- easier to deal with; no difficulties in parameter estimation- not suitable for model following control:(inversion of time delay amounts to time lead – not realizable
Extension of 6h rotor degrees of freedom:1) implicit first-order approximation of the main rotor2) explicit equations for the rotor degrees of freedom
Basic equation for roll response can be written as:
step input results in a step response in the roll acceleration
controlpp LpLp δδ+−=& Eq. 12.45
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/61Dr. Ravindra Jategaonkar
Rotorcraft Modeling (7)High bandwidth models -- implicit first-order approximation
First order approximation of main rotor:based on high correlation between the flapping motion of the rotor tip path plane (lateral and longitudinal flapping) and the body-fixed rotational (roll and pitch) accelerations.
Correlation between roll acceleration and lateral flapping(rigid rotors and high hinge offset):
where is the lateral flapping, the control input at the blade root, and a flapping time constant.
This coupled differential equation indicates that a step into the control input leads to a first order response of the rotor itself coupled with the body response driven by the rotor flapping.
11 bLp b=&
controlb
b
b
Lpbb δ
ττδ+−−= 11
1&
1b controlδbτ
Eq. 12.47
Eq. 12.46
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/62Dr. Ravindra Jategaonkar
Rotorcraft Modeling (8)High bandwidth models -- implicit first-order approximation
Differentiating (12.46), substituting (12.47) and then using again (12.46)leads to a second order differential equation for rotor/body motion:
Which can be equivalently formulated as:
where denote the new set of lateral system parameters.
Thus, either the two first order equations (12.46) and (12.47) are now used to model the roll motion and lateral flapping, or depending on the application in the control system design the equivalent second order equation (in roll rate p), Eq. (12.49), can be used for system identification.
controlb
bbb
bb
LLpLpbLp δ
ττδ1
1111
+−−== &&&&
controlppp LpLpLp δδ~~~)( +++= L&&& &
()~L
Eq. 12.48
Eq. 12.49
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/63Dr. Ravindra Jategaonkar
Rotorcraft Modeling (9)High bandwidth models -- implicit first-order approximation:
The derivatives appearing in Eq. (12.49) are not the same as those in the classical rolling motion equation of the rigid-body motion.
Thus, when we use Eq. (12.49) for parameter estimation purposes, the classical parameters gets scaled through the flapping time constant and the flapping effects appear indirectly through these scaled parameters.
Incorporation of these second order models in the parameter estimation is little tricky, because the estimation programs require models postulated as first order differential equations. This is elegantly done by treating and as the state variables, leading to a state vector given by:
compared to that for pure rigid-body:
b
bb
b
ppb
b
pp
bp
LLLLL
LLL
ττττδδ
δ1
1~,~,1~ ==−=−=−=&
Trqqppwvux ][ θφ&&=
p& q&
Trqpwvux ][ θφ=
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/64Dr. Ravindra Jategaonkar
( )pp L p L tδδ= +&
Conventional first order- rigid body only -
bp L b=&
( )b L tb pδ δτ
− += −&
Extended second order- rigid body & first order rotor-
( )p pp L p L p L tδ δ= + +&% % %&& &
Equivalent second order rigid body
Extended model formulation: Summary
Rotorcraft Modeling (10)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/65Dr. Ravindra Jategaonkar
Rotorcraft Modeling (11)High bandwidth models -- implicit first-order approximation :The main advantage of this approach is that the extended model implicitly represents dynamics of the rotor degrees of freedom as a first order system. Together with the 8th order state vector of the rigid-body motion defined earlier, above approximation of the rotor coupling in the pitch and roll leads to a 10th order model, covering a wider frequency range. As rotor dynamics are implicitly modeled, the time delays are significantly reduced to the pure influence of the actuator dynamics.
High bandwidth models -- Explicit rotor degrees of freedom:Considering the blade flapping motions in terms of the tip path plane variables, the states of the rigid-body motion are extended by:
where and denote the longitudinal and lateral flapping, and the coning motion, each of which is modeled as a second order system, leading to an extended model with nine degrees of freedom.
Ta abaabax ][ 011011 &&&=
1a 1b 0a
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/66Dr. Ravindra Jategaonkar
State Matrix States
= +
rigid body
FUSELAGE
ROTOR
rotor
body/rotorcoupling
rotor / bodycoupling
x
y
o
TR
δ
δ
δδ
ControlMatrix Controls
a0.
v.
p.
q.
u.
w.
r.
.
..
.
.
a1.
b1
.
.
..
u
v
w
p
q
r
a1
b1
a0
Extended Model Structure Approach
Rotorcraft Modeling (12)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/67Dr. Ravindra Jategaonkar
BO 105 Time Histories (Linear Simulation and Flight Data)
Control Inputlateralstick
%
-10
10
Flight Data6 DoF Rigid Body
rollrate
rad/s
-0.3
0.3
rollacc.
2.0
-2.0
rad/s2
0 10time
s5
6 DoF Rigid Body
time
lateralflapping
.015rad/s
0 10s- .015 5
rollrate
rad/s
-0.3
0.3
rollacc.
2.0
-2.0
rad/s2
9 DoF Rigid Body + Rotor
9 DoF Rigid Body + Rotor
Rotorcraft Modeling (13)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/68Dr. Ravindra Jategaonkar
BO 105 Time Histories - Quality Control
9 DoF Rigid Body/Rotor Roll Acceleration
6 DoF Rigid Body Roll Acceleration
Rotorcraft Modeling (14)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/69Dr. Ravindra Jategaonkar
Rotorcraft Modeling (15)
Frequency
dB
10
deg
Mag
nitu
dePh
ase
rad/s
-1045
0
-900.3 1 10
0
Rigid-body 6 DOF GenericNL model
9 DOF withrotor dynamics
Rigid-body 6 DOFGenericNL model
9 DOF withrotor dynamics
Boundaries of unnoticable dynamics
Rat
io o
f rol
l rat
e re
spon
ses
BO 105 model validation in frequency domain
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/70Dr. Ravindra Jategaonkar
Rotor Wake Modeling
Accurate Prediction of Off-axis Response- At hover and at low speeds;a long standing problem
- asymmetrical vortex compressionand dilation act on the induced velocity field.
- Gyroscopic behavior leads tostrong cross- coupling effectsdue to wake distortion
- Complex models based on geometricallyprescribed or free wake formulationwith discrete vortices; or equivalent vortex ring/sheet formulation.
- Simpler models based on local phenomenon -- Parametric extensions for the dynamics of the inflow
Pure Hover
Pitching motion in Hover
Rotorcraft Modeling (16)
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/71Dr. Ravindra Jategaonkar
Rotor Wake Modeling
M :Apparent mass matrix associated with theacceleration terms from momentum theory
L : gain matrix, λ (= [λ0, λs, λc]T) the inflow ratiodescribing the first harmonic terms
c (= [cT, c1, cm]T): rotor load coefficients wrt rotor thrust and aerodynamic pitch and roll moment,
Ω: main rotor rotation speedKp and Kq: The wake distortion parameters for
longitudinal and lateral distribution of theinduced velocity.
The last term on the RHS is the parametric term that feeds back the total roll and pitch rates of the rotor tip path plane wrt to the surrounding air to the induced velocity distribution over the rotor disk.
Estimate Kp and Kq
4444444444 34444444444 21
&
&
444 3444 21
&
dynamicsdistortionWakeInflow
dynamicsInflowPetersPitt
cqqKsppKLcLM
+
⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢
⎣
⎡
−
−−
Ω+=
−+
)(
)(0
1ˆ11ˆ
&
β
βλλ
Rotorcraft Modeling (17)
Parametric extension of Pitt and Peters dynamic wake model:
Theoretical estimates of wake distortion parameters:
)/( RVH Ω=μVH: forward speed in m/s, Ω: main rotor rotation speed in rad/s,R: rotor radius in meter
.
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/72Dr. Ravindra Jategaonkar
Rotor Wake Modeling:
Hover:
Simulation fidelity withTheoretical and flightIdentified WD parameters:
Rotorcraft Modeling (18)
Lateral Inp
utLongitudinal
Input
Roll Rate
Pitch Ra
te
deg/s
deg/s
deg
20
0
-30
30
0
20
-20
2
0
-3
time20 25 30 35
-20
deg4
0
-1
2
s
Flight case #1 Flight case #2with theoreticalWD-parameters
with flightidentified WD parameters
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/73Dr. Ravindra Jategaonkar
Rotor Wake Modeling:
Hover:
Off axis response without (i.e. inflow dynamics only) and with flight identified WD parameters
Rotorcraft Modeling (19)
20
10
-10
-20
60 64 68stime
deg/s
Inflow and WakeDistortion
0
20
10
0
-10
-20
60 64 68stime
deg/s
PitchRate
Inflow Dynamics
Flight DataSimulation
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/74Dr. Ravindra Jategaonkar
Rotor Wake Modeling:Forward speed of 40 m/s:
Kp = 1.1; Kq = 1.6
Estimates do not conform to the wake prediction theory (they should be roughly zero).
classical issue often faced in modeling applying system identification methods:
estimated parameters do not represent the wake distortion phenomenon which mainly occurs at hover and at low speeds, rather they account for some other unmodeled effects, for example those resulting from rigid blade formulation as assumed in the present investigations.
Rotorcraft Modeling (20)
with PWD
without PWD
time
Lateral Inp
utLo
ngitu
dina
lInpu
tRo
ll Rate
Pitch Ra
te
deg/s
deg/s
deg
deg
2
15
0
-10
20
0
-20
-40
-1
-3
-5
4
00 2 4 6 8 10s
Flight case #1 Flight case #2
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/75Dr. Ravindra Jategaonkar
References (1)Jategaonkar, R. V., Flight Vehicle System Identification: A Time Domain Methodology,Volume 216, AIAA Progress in Astronautics and Aeronautics SeriesPublished by AIAA Reston, VA, Aug. 2006, ISBN: 1-56347-836-6http://www.aiaa.org/content.cfm?pageid=360&id=1447
Hamel, P. G., and Jategaonkar, R. V., “Evolution of Flight Vehicle System Identification,”Journal of Aircraft, Vol. 33, No. 1, 1996, pp. 9-28.
Hamel, P. G. and Jategaonkar, R. V., The Role of System Identification for Flight Vehicle Applications -Revisited”, RTO-MP-11, March 1999, Paper No. 2.
Jategaonkar, R. V., Fischenberg, D., and von Gruenhagen, W., “Aerodynamic Modeling and System Identification from Flight Data – Recent Applications at DLR,”Journal of Aircraft, Vol. 41, No. 4, 2004, pp. 681-691.
Jategaonkar, R. V., Mönnich, W., Fischenberg, D., and Krag, B. “Identification of Speed Brake, Air-Drop,and Landing Gear Effects from Flight Data”, Journal of Aircraft, Vol. 34, No. 2, March-April 1997, pp 174-180.
Jategaonkar, R. V., Mönnich, W., Fischenberg, D., and Krag, B. “Identification of C-160 Simulator Data Base from Flight Data”, Proceedings, 10th IFAC Symposium on System Identification, Copenhagen, Denmark,July 1994, pp. 3.67-3.74.
Jategaonkar, R.V., Mönnich, W., “Identification of DO-328 Aerodynamic Database for a Level D FlightSimulator”, AIAA 97-3729, 1997.
Mönnich, W., Jategaonkar, R.V., “Database Development for Level D Simulators - Lessons Learned”,RTO SCI Symposium on ‘System Identification for Integrated Aircraft Development and Flight Testing’,May 5-7, 1998, Madrid, Spain, Paper 14.
AIAA Short Course: Flight Vehicle System Identification in Time Domain, Aug. 2006 Examples/76Dr. Ravindra Jategaonkar
N.N., “Airplane Simulator Qualification”, FAA Advisory Circular, AC 120-40C, Interim Version, Jan. 1995.
N.N., “Joint Aviation Requirements - Aeroplane Flight Simulators”, JAR-STD 1A, Westward Digital Ltd.,Cheltenham, England, April 1997.
Jategaonkar, R. V., Behr, R., Gockel, W., and Zorn, C., “Data Analysis of Phoenix RLV Demonstrator Flight Test,” AIAA Paper 2005-6129, Aug. 2005.
Rohlf, D., Plaetschke, E., Weiss, S., “X-31A System Identification Applied to Post Stall Flight - Aerodynamics and Thrust Vectoring”, AGARD CP-548, March 1994, Paper 14.
Weiss, S., Friehmelt, H., Plaetschke, E., and Rohlf, D., “X-31A System Identification using Single Surface Excitation at High Angles of Attack”, Journal of Aircraft, Vol. 33, No. 3, May-June 1996, pp. 485-490.
Rohlf, D., “Direct Update of a Global Simulator Model with Increments via System Identification”,
RTO SCI Symposium on ‘System Identification for Integrated Aircraft Development andFlight Testing’, May 5-7, 1998, Madrid, Spain, Paper 28.
Tischler, M.B., “System Identification Methods for Aircraft Flight Control Development and Validation”,NASA TM 110369, Oct. 1995.
Kaletka, J., von Grünhagen, W., “System Identification of Mathematical Models for the Design of a Model Following Control System”, Vertica, Pergamon Press, Oxford, Vol. 13, No. 2, 1989, pp. 213-228.
Anon, “Rotorcraft System Identification”, AGARD, AR280, Sept. 1991.
Hamel, P.G., Kaletka, J., “Rotorcraft System Identification - An Overview of AGARD FVP Working Group 18”,AGARD CP-552, 1995, Paper 18.
N.N., “Helicopter Simulator Qualification”, FAA Advisory Circular, AC 120-63, Oct. 1994.
Rohlfs, M., von Grünhagen, W., Kaletka, J., “Nonlinear Rotorcraft Modeling and Identification”, RTO SCI Symposium on ‘System Identification for Integrated Aircraft Development andFlight Testing’, May 5-7, 1998, Madrid, Spain, Paper 23.
References (1)