This document summarizes a master's thesis defense presentation on flutter suppression of a flexible flying-wing UAV. The presentation covers developing a flight dynamics model that includes local lift and acceleration outputs, analyzing aeroelastic modes including the body freedom flutter mode, and designing two controllers using different outputs and comparing their performance and robustness. Controller 2, which uses local lift coefficient as the output, achieves greater damping of the body freedom flutter mode with more robust stability margins compared to Controller 1, which uses local acceleration.

1. Flutter Suppression of a Flexible
Flying-Wing UAV Using the Leading
Edge Stagnation Point Sensor
As presented to:
Adrià Serra Moral
August 26, 2016
MASTERS DEFENSE COMMITTEE

2. OUTLINE
 Introduction
 Aerodynamic Observable
 Flight-Dynamics Modeling
 Active flutter suppression
 Conclusions
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3. Introduction: Flexible Aircraft
Why Flexible Aircraft?
DESIRE TO MAKE AIRCRAFT MORE FUEL
EFFICIENT AND INCREASE PERFORMANCE
𝐝𝐖
𝐝𝐭
=
−𝐖
𝐂 𝐋
𝐂 𝐃
𝐈 𝐬𝐩
[P.M. Sforaza, 2014]
𝑰 𝒔𝒑
𝑪 𝑳
𝑪 𝑫
𝑾 Aircraft Gross Weight [lbs]
Total Lift Coefficient [unitless]
Total Drag Coefficient [unitless]
Engine’s Specific Impulse [
lbs
lbs/sec
]
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4. How can we reduce fuel burn? (↓) Decrease W
 Use low-density composite materials
for structural design
(↑) Increase Isp
 Make engines more fuel-efficient
CD = CDo
+
CL
2
πARe
Recall:
AR =
wingspan2
Area
(↓) Decrease CD
 Reduce CDo
 Fuselage with smaller diameter
 Airfoil with lower thickness
Introduction: Flexible Aircraft
𝐝𝐖
𝐝𝐭
=
−𝐖
𝐂 𝐋
𝐂 𝐃
𝐈 𝐬𝐩
[P.M. Sforaza, 2014]
 Increase AR
 Longer and more slender wings
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5. Source: www.nasa.gov
Coupling
Aeroelastic
Modes
Rigid Body
Modes
(+) frequency
[L. Meirovitch, 2004]
Introduction: Aeroservoelasticity
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6. Introduction: Active Flutter Suppression
Body-Freedom Flutter
 One Solution: Active Flutter-Suppression
Example of Unsuppressed Flutter and Catastrophic Structural Failure
30 m/s
August 25, 2015 University of Minnesota
[J. Theis, 2016]
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7. AFS: Current Approach
Gust(s)
Aerodynamics
Loads
Structural
Loads
Deformation
Oscillations
Sensor
Measurements
Control Law(s)
Actuators
Aerodynamic
Observable
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8. Aerodynamic Observable
SenflexR Hot-Film Sensor
[V. Suryakumar, 2015]
Senflex® Hot-Film Sensor→ Measure LESP
[A. Mangalam, 2014]
[V. Suryakumar, 2015]
𝑪 𝑳 = 𝒇(𝑳𝑬𝑺𝑷, 𝜹, 𝒘𝒊𝒏𝒈 𝒈𝒆𝒐𝒎𝒆𝒕𝒓𝒚)
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9. Thesis Objectives:
II. Design 2 Controllers:
i. Controller 1: uses the local acceleration (aZ) as system output
ii. Controller 2: uses the local lift coefficient (CL) as system output
I. Develop a flight dynamics model of a flexible drone that includes the local
vertical acceleration (aZ) and the local lift coefficient (CL) as system outputs
III. Compare the performance and the robustness of the two controllers
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10. Mini MUTT (Multi Utility Technology Testbed)
• Wingspan = 10 ft (~3m)
• Mass = 14.7 lbs (6.7 kg)
• 8 Control Surfaces
• 1 Electric Motor with
Pusher Propeller
Lockheed Martin’s
BFF [J. Beranek, 2010]
Mini-MUTT
Mini-MUTT: The Flying-Wing UAV
[http://www.uav.aem.umn.edu/wiki/Infrastructure/Aircraft]
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11. “Flight-Dynamics” Modeling Approach
[D. Schmidt, 2015]
Structural Vibration
Solution (FEM)
Aeroelastic
Influence
Coefficients
Aeroelastic
Dynamic Model
Flight Test
Rigid-Body
Aerodynamics
Mass/Inertia
Properties
Flight
Condition
Rigid-Body
Dynamic Model
Traditional
Rigid AircraftFlexible Effects
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12. Mini-MUTT: State-Space Model
x = Ax + Bu
A =
ARR ARE
AER AEE
xT
= [ urig αrig θrig qrig η1 η1 η2 η2 η3 η3 ]
uT = [ δ1 δ2 δ3 δ4 ]
Rel. Wind (Uo)
urig
αrig
qrig
θrig
x
z
y
Dynamic Model of Longitudinal Dynamics
B =
BR
BE
[D. Schmidt, 2015]
X, Z, M: Rigid dimensional coefficients Zη, Mη, Ξη: Aeroelastic dimensional coefficients
ζk: Damping ratio of kth structural mode ωk: Eigenfrequency of kth structural mode
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13.  Input: Symmetric deflections of L1R1 to L4R4 (δ1 to δ4)
 Output: CL(Ysensor) and az(Ysensor)
Mini-MUTT: State-Space Model
Sensor-Output Model Y = Cx + Du
xT = [ urig αrig θrig qrig η1 η1 η2 η2 η3 η3 ] uT
= [ δ1 δ2 δ3 δ4 ]
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14. Model LESP Output
Follow approach in Dave Schmidt’s Book “Modern Flight Dynamics”
[D. Schmidt, 2011]
az ysensor = Uo αrig − Uoqrig + ∆xsensorqrig +
i=1
3
νzi
ysensor ηi
αw ysensor = αrig −
∆xsensor qrig
Uo
+ (
i=1
3
ν′
zi
ysensor ηi +
i=1
3
νzi
ysensor ηi
Uo
)
CL ysensor = CLα
ysensor αw ysensor +
i=1
3
CLηi
ηi +
i=1
3
CLηi
ηi +
i=1
4
CLδi
δi
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15. Model LESP Output
Mode Shapes of Interest
[Obtained using structural FEM2.1 developed by Virginia TECH]
νz1
ysensor
ν′z2
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16. Modes and Flutter Analysis
Eigenvalue
Damping
Ratio
ωn
[rad/s]
Mode
Branch
0.0038 ± 0.35i -0.0109 0.348 Phugoid
-16.9 ± 15.9i 0.729 23.2 ESP
0.735 ± 29.5i -0.0249 29.5 BFF
-1.38 ± 67.8i 0.0204 67.8 2nd ASE
-5.71 ± 122i 0.0466 123 3rd ASE
Open-Loop Data at Uo = 33.5 m/s
 Design Flutter Suppression Controller beyond 30 m/s
 Uo = 33.5 m/s
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17. Active Flutter Suppression
Objective:
Benefits:
 Suppress BFF mode without altering the handling qualities
 Identically Located Acceleration and Force [J. Wykes, 1997]
 “ILAF”-like approach [D. Schmidt, 2016]
 Easily designed using root-locus methods
 Compare the performance and robustness of the CL-output controller to the
az-output controller
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HFWO HFWO

18. Control Effectors and Sensor Selection
 Use root locus to obtain and compare the maximum ζBFF that can be achieved
in closed-loop for:
 Different sensor locations: y = 20 in, y = 40 in, y = 57.5 in (from CG)
 Different control inputs: u = δ1 (i = 1), u = δ4 (i = 4)
 Note: δ2 and δ3 are reserved uniquely for the pilot (or autopilot)
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HFWO HFWO

19. Control Effectors and Sensor Selection
Sensor → Actuator Pair
y-location
[in]
aZ(y) → δ1
Closed-Loop ζ
CL(y) → δ1
Closed-Loop ζ
aZ(y) → δ4
Closed-Loop ζ
CL(y) → δ4
Closed-Loop ζ
BFF Mode
Open Loop
ζBFF = -0.0249
20 0.30 0.474 0.0902 0.111
40 0.066 0.281 0.10 0.12
57.5 0.034 0.22 0.12 0.16
 ILAF-like: Damping increases as the
sensor is placed closer to the actuator
 Sensor location y = 20 in and control
effector δ1 achieve greatest ζBFF
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𝐲 𝛅 𝟏
∈ 𝟑, 𝟏𝟓 𝐢𝐧 𝐲 𝛅 𝟒
∈ 𝟒𝟑, 𝟓𝟕 𝐢𝐧

20. Control Effectors and Sensor Selection
 Washout Filter: HFWO(s) =
s
s+27
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21. Active Flutter Suppression
Select Controller Gain (Kp) such that:
Comparison Procedure:
I. All 3 aeroelastic modes must be stable in closed-loop
II. The damping ratio of the “Elastic Short Period” mode must be within:
I. Set ζESP of Controller 1 = ζESP of Controller 2 and compare performance (ζBFF)
*Note: damping ratio of other modes also included for completeness
II. Set ζBFF of Controller 1 = ζBFF of Controller 2 and compare robustness (GM, PM)
0.6 ≤ ζESP ≤ 0.85
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22. Closed-Loop Results: System + Filter
BFF Mode 2nd Aeroelastic Mode 3rd Aeroelastic Mode
Controller
Open-Loop
ζBFF
Closed-Loop
ζBFF
%-
Increase
Open-Loop
𝛇 𝐀𝐄 𝟐
Closed-Loop
𝛇 𝐀𝐄 𝟐
%-
Increase
Open-Loop
𝛇 𝐀𝐄 𝟑
Closed-Loop
𝛇 𝐀𝐄 𝟑
%-
Increase
1 ( aZ )
-0.0249
0.2
21 0.0204
0.0267
-30 0.0466
0.0671
56
2 ( CL ) 0.242 0.0189 0.105
Performance:
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Recall: ζESP of Controller 1 = ζESP of Controller 2 = 0.60

23. Closed-Loop Results: System + Filter
Controller 𝐊 𝐩-Gain
Gain Margin
(dB)
Phase Margin
(deg.)
Gain-Crossover
Frequency
(rad/s)
1 ( aZ ) 0.00172 -16 4.3 -87 47 38
2 ( CL ) 0.523 -21 32 -89 65 38
Robustness:
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Recall: ζBFF of Controller 1 = ζBFF of Controller 2 = 0.20

24. Closed-Loop Results: Bode Diagram
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25. Closed-Loop Attitude Response
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26. Parasite Dynamics
HFLAP s =
96710
s2 + 840s + 96710
HSENS s =
2π35
s + 2π35
HDELAY s = e−0.0132S
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27. Parasite Dynamics Summary
𝛇 𝐁𝐅𝐅
GM
[dB]
PM
[deg.]
Controller Value
%-
Increase
Value
%-
Increase
Value
%-
Increase
1. System + Filter
1 ( aZ ) 0.2
21
4.3
580
47
42
2 ( CL ) 0.242 29.23 67
4. System + Filter +
Flap + Sensor + 13.2
ms Delay
1 ( aZ ) 0.0152
285
2.77
104
5
240
2 ( CL ) 0.0585 5.66 17
 Recall: ζESP of Controller 1 = ζESP of Controller 2
 Controller 2 has better performance and robustness
 Here, GM and PM included for extra consideration
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28. Summary and Conclusion
 Developed a flight-dynamics model (in MATLAB) of a flexible flying-wing
drone that includes both the local aZ and the local CL at any point across
the wingspan as system outputs.
 Showed that, using a simple P-controller, CL-output can achieve greater
damping ratios of the BFF mode with more robustness
 Showed that classical control architectures with ILAF-like designs have
potential to suppress flutter modes
 Gained better understanding of cause-and-effect relationships between
LESP sensor and physics of the aircraft in flutter
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29. Future-Work
 Investigate flutter suppression using energy methods, i.e. a
control law that takes both measurements (CL+az)
 Study aerodynamic observable methods with other multivariable
control strategies (e.g., Hꝏ, LQG,…)
 Test results on an experimental setup
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30. Q&A
Acknowledgements
 NASA NRA NNX15CD07C
Subcontract to TAO Systems
for funding this research
 Dr. Dave Schmidt for his
insight, guidance, and
intuition throughout the
entire process
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31. References
P.M. Sforza. Commercial Airplane Design Principles. Elsevier aerospace engineering series.
2014
L. Meirovitch and I. Tuzcu. Time simulations of the response of maneuvering flexible
aircraft. Journal of Guidance, Control, and Dynamics, 27(5):814-828, Sep. 2004
Schmidt, D. K., Stability Augmentation And Active Flutter Suppression Of A Flexible Flying-
Wing Drone, Journal of Guidance, Control, and Dynamics, 2015
D. Schmidt. Modern Flight Dynamics. McGraw-Hill Education, 2011
V.S. Suryakumar, Y. Babbar, T.W. Strganac, and A. Mangalam. An unsteady aerodynamic
model based on the leading-edge stagnation point. AIAA Applied Aerodynamics
Conference, Jun 2015.
A. Mangalam and M. Brenner. Fly-by-feel sensing and control: Aeroservoelasticity. AIAA
Atmosphere Flight Mechanics Conference, Jun 2014.
J. Theis, P. Harald, and P. Seiler. Robust control design for active flutter suppression. AIAA
Atmospheric Flight Mechanics Conference, 2016
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33. Aerodynamic Observable
Vout = 1 +
R3
R1
+
R3
RHF
VHF VHF =
R1
R2
V1
 Vout increases as local airstream velocity increases
 Local airstream velocity at LESP = 0
 Vout at LESP is a local minimum → measure LESP
[http://www.taosystem.com]
SenflexR Hot-Film Sensor
[V. Suryakumar, 2015]
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34. Modes and Flutter Analysis
“Elastic Short Period” Mode
1st Aeroelastic Mode (BFF Mode)
Phasor Analysis
𝛉 𝐄 𝟏
𝐪 𝐫𝐢𝐠
𝛂 𝐫𝐢𝐠
𝐪 𝐫𝐢𝐠
𝛉 𝐄 𝟏𝛉 𝐄 𝟐
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35. Closed-Loop: Controller Effort
Uo = 33.5 m/s
1,000 ft. AGL
 Note: Modest control effort < 1 degree
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36. System + Flaps
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37. System + Flaps + Sensor
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38. System + Flap + Sensor + Delay
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39. Parasite Dynamics: Time Delay
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40. Attitude Response for 13.2 ms Delay
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41. Closed-Loop: Controller Effort
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42. Parasite Dynamics Summary: Performance
𝛇 𝐁𝐅𝐅 𝛇 𝐀𝐄 𝟐
𝛇 𝐀𝐄 𝟑
GM
[dB]
PM
[deg.]
Controller Value
%-
Increase
Value
%-
Increase
Value
%-
Increase
Value
%-
Increase
Value
%-
Increase
1. System +
Filter
1 ( aZ ) 0.2
21
0.0267
-30
0.0671
56
4.3
580
47
42
2 ( CL ) 0.242 0.0188 0.105 29.23 67
2. System +
Filter + Flaps
1 ( aZ ) 0.188
17
0.0289
-44
0.0225
318
5.87
353
37
48
2 ( CL ) 0.22 0.0160 0.0940 26.6 55
3. System +
Filter + Flaps
+ Sensors
1 ( aZ ) 0.164
11
0.0248
-40
0.0297
93
5.3
81
28
61
2 ( CL ) 0.182 0.0150 0.0572 9.6 45
4. System +
Filter + Flap
+ Sensor +
13.2 ms
Delay
1 ( aZ ) 0.0152
285
0.0190
-11
0.0455
-34
2.77
104
5
240
2 ( CL ) 0.0585 0.0170 0.030 5.66 17
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43. Conclusion: Insight
ILAF: sensor and control input must be away from a node
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44. Energy Methods
Importance of FBF?
Typical Aeroservoelastic (ASE) system:
𝑨 𝒒 + 𝝆𝑼 𝒐 𝑩 + 𝑫 𝒒 + 𝝆𝑼 𝒐
𝟐
𝑬 𝒒 = 𝑭𝒖 + 𝑮𝝎 𝒈
A := Struct. Inertia B := Aero. Damping C := Aero. Stiffness
D := Struct. Damping E := Struct. Stiffness F := Control Excitation
G := Gust Load Excitation u := Control input ωg := Wind Gust input
Note:
i. Model-Dependent System → Uncertainties
ii. Control using inertial measurements (accels.) only → Structures lag Aerodynamics
[Wright and Cooper, 2014]
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45. Energy Methods
Importance of FBF?
Physics of Flutter:
𝑾 𝒔 =
𝒕
𝒕+𝑻
𝑭 𝑨 ∙ 𝒗 𝒅𝒕 < 𝟎
NOTE:
i. Since 𝑭 𝑨 and 𝒗 are measurable, we can compute Ws (Model-Independent)
ii. Design Control System based on Load-Output Feedback.
𝑭 𝑨 := Aerodynamic Force (Lift)
𝒗 := Local velocity
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46. How Good is LESP CL Output?
From wind tunnel tests at Texas A&M University
using the model as described in [V. Suryakumar, 2015]
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