Download - Micro Air Vehicle - MAV Micro Air Vehicle
Krzysztof SibilskiKrzysztof SibilskiKrzysztof SibilskiKrzysztof SibilskiKrzysztof SibilskiKrzysztof SibilskiKrzysztof SibilskiKrzysztof SibilskiWroclaw University Wroclaw University of Technology, Department of of Technology, Department of Aerospace Aerospace EngineeringEngineering
Air Force Institute of Technology Air Force Institute of Technology
MODELING AND SIMULATION OF MODELING AND SIMULATION OF
FLAPPING WINGS MICROFLAPPING WINGS MICRO--AERIALAERIAL--
VEHICLES FLIGHT DYNAMICS VEHICLES FLIGHT DYNAMICS
Micro Air VehicleMicro Air Vehicle -- MAVMAV
Technical Objectives:Technical Objectives: Develop flight enabling Develop flight enabling
technologiestechnologies..
Develop and demonstrateDevelop and demonstrateMicro Air Vehicles capable of Micro Air Vehicles capable of sustained flight and useful sustained flight and useful military missionsmilitary missions..
Military Relevance:Military Relevance:
Enhanced Situational Awareness for Enhanced Situational Awareness for
Small Units OperationsSmall Units Operations
Platoon level assetPlatoon level asset
Enables new missions in emergingEnables new missions in emerging
warfightingwarfighting environmentsenvironments
Urban operationsUrban operations
Potential users: Army, Marines, Air Potential users: Army, Marines, Air
Force, Navy, Special Operations Force, Navy, Special Operations
ForcesForces
Micro Air VehicleMicro Air Vehicle -- MAVMAV
Small air vehicle no larger Small air vehicle no larger than 15 cm in any than 15 cm in any dimension.dimension.
Capable of performing a Capable of performing a useful military mission at useful military mission at an affordable cost.an affordable cost.
Non Non Military Relevance:Military Relevance:
•• Outdoor NBC emergencyOutdoor NBC emergency reconnaissancereconnaissance
•• Crowd controlCrowd control
•• Suspect facilities surveillance Suspect facilities surveillance
•• Snap inspection of pollutionSnap inspection of pollution
•• Road accident documentationRoad accident documentation
•• Urban traffic managementUrban traffic management
•• Search for survivorsSearch for survivors
•• Pipeline inspectionPipeline inspection
•• High risk indoor inspectionHigh risk indoor inspection
•• Space explorationSpace exploration
MAV MissionsMAV Missions –– military applicationmilitary application
MAV HOST
MAV’s can deliverunattended
surface sensors
Interior Operationand Surveillance?
Interior Operationand Surveillance?Interior Operation?Interior OperationInterior Operation?
Sensor PlacementSensor PlacementSensor Placement
ReconnaissanceReconnaissanceReconnaissance Chemical CloudChemical CloudTracked by MAVTracked by MAV
Sensor detectsSensor detectsPPM PPM -- PPBPPB
MAV provides situational awareness,Provides beacon for rescue operations.
MAV provides situational awareness,MAV provides situational awareness,Provides beacon for rescue operations.Provides beacon for rescue operations.
Urban OperationsUrban Operations
MAV Assisted Pilot RescueMAV Assisted Pilot Rescue
BioBio--Chemical SensingChemical Sensing
““OverOver--thethe--hillhill”” ReconnaissanceReconnaissance
33
11
11
22
33
44
22
44
Miltary application – antiterrorist missionMiltary applicationMiltary application –– antiterrorist missionantiterrorist mission The Micro Air Vehicle Flight Regime The Micro Air Vehicle Flight Regime
Compared to Existing Flight VehiclesCompared to Existing Flight Vehicles
Speed
Hover
Agility
Covertness
High Degree of Integration and MultifunctionalityHigh Degree of Integration andHigh Degree of Integration and MultifunctionalityMultifunctionality
Range
Technical ChallengesTechnical Challenges
Low Re Aerodynamics and ControlLow Re Aerodynamics and Control
Lightweight Power and PropulsionLightweight Power and Propulsion
Autonomy navigation, Autonomy navigation,
guidance and controlguidance and control
UltraUltra--light sensorslight sensors
and communicationsand communications
IntegrIntegrationation of MAV systems of MAV systems
What isWhat is BiomimeticsBiomimetics??BiomimeticsBiomimetics is the abstraction a good design from Nature. is the abstraction a good design from Nature.
Scientists and engineers are increasingly turning to nature for Scientists and engineers are increasingly turning to nature for inspiration. The solutions arrived at by natural selection are oinspiration. The solutions arrived at by natural selection are often a ften a good starting point in the search for answers to scientific and good starting point in the search for answers to scientific and technical technical problems. Equally, designing and buildingproblems. Equally, designing and building bioinspiredbioinspired devices or devices or systems can tell us more about the original animal or plant modesystems can tell us more about the original animal or plant model.l.This lecture is focused onThis lecture is focused on BioinspirationBioinspiration && BiomimeticsBiomimetics in design of a in design of a new generation of flying vehicles sonew generation of flying vehicles so caaledcaaled Micro Aerial Vehicles Micro Aerial Vehicles ((MAVsMAVs).). MAVsMAVs are not a toys.are not a toys. MAVsMAVs are new generation of autonomous are new generation of autonomous flying robots.flying robots.Our research involved:Our research involved:
-- the study and distillation of principles and functions foundthe study and distillation of principles and functions foundin biological systems flight principles that have beenin biological systems flight principles that have beendeveloped through evolution,developed through evolution,-- application of this knowledge to produce novel and excitingapplication of this knowledge to produce novel and excitingbasic technologies and new approaches to solving scientificbasic technologies and new approaches to solving scientificproblems ofproblems of fligtfligt..
Insects Insects -- over 30 000 000 specimensover 30 000 000 specimens
Aerodynamically, the bumblebee can not fly.The bumblebee’s body is too heavy and its wings
span too small !!!
But it does not know that. That is why it flies.
Aerodynamically, the bumblebee can not fly.The bumblebee’s body is too heavy and its wings
span too small !!!
But it does not know that. That is why it flies.
Wais – Fogh mechanismWais Wais –– FoghFogh mechanismmechanism
Wais – Fogh mechanismWais Wais –– FoghFogh mechanismmechanism Wings rotation (rotational lift)Wings rotation (rotational lift)Wings rotation (rotational lift)
LEX vortexLEX LEX vortexvortex LEX LEX vortexvortex
Insects in flightInsects in flightEntomopter & insect wingsEntomopter & insect wings
Wing MotionWing Motion
Not simply up and down -much more complex!
Wingtip trajectoriesWingtip trajectories
Wing MotionWing Motion
Can consider as motion as Can consider as motion as being composed of three being composed of three different rotations flapping, different rotations flapping, lagging, and featheringlagging, and feathering
Three Hinges of the Wing Three Hinges of the Wing Apparatus.Apparatus.
Horizontal (flapping)Horizontal (flapping)Vertical (lagging)Vertical (lagging)TorsionalTorsional (feathering)(feathering)
EachEach insect insect hinge occurs at the intersection of a vein and foldhinge occurs at the intersection of a vein and fold
Helicopter articulated hub
Hinges of an entomopter wing
HingesHinges
The horizontal hinge The horizontal hinge -- 1 1 - occurs near the base of the wing occurs near the base of the wing next to the first next to the first axiliary scleriteaxiliary sclerite-- this hinge allow the wingthis hinge allow the wingto flap up and downto flap up and down
The vertical hinge The vertical hinge -- 2 2 -- located at the base of the located at the base of the radial vein near the second radial vein near the second axillary scleriteaxillary sclerite (2AX)(2AX)-- responsible for theresponsible for thelagging motion of the winglagging motion of the wing
The The torsionaltorsional hinge hinge -- 33-- more complicatedmore complicatedinteraction of interaction of scleritescleriteand deformable foldsand deformable folds
Insect wings
kinematics
Insect wings kinematicsInsect wings kinematicsInsect wings kinematicsEntomopter’s gears
Dr. Zbikowski Cranfield University
EntomopterEntomopter’’ss gearsgears
Dr.Dr. Zbikowski CranfieldZbikowski Cranfield UniversityUniversity
Entomopter gears Insect and bird enginesInsect and bird engines a) a) Insect Insect „„engineenginess””
b) anatomyb) anatomy
c) birds c) birds „„engineengine””
DD) ) entomopterentomopter engineengine
cc bb
aa
ddd
Unconventional propulsion –artificial muscels
From NASA websiteFrom NASA websiteFrom NASA website
RotorcraftRotorcraft primaryprimary
aerodynamic controlsaerodynamic controls
Fixed wing aircraft primaryFixed wing aircraft primary
aerodynamic controlsaerodynamic controls
Fixed & rotary wings aircraft aerodynamic controlsFixed & rotary wings aircraft aerodynamic controls
Entomopter vs. HelicopterEntomopter Entomopter vsvs. . HelicopterHelicopter
Analogies:-Control by change of thrust position and thrust magnitude
AnalogiesAnalogies::--Control Control byby change of thrust positionchange of thrust position and thrust magnitudeand thrust magnitude
Differences:
- aerodynamic loads are complex function of wings configuration and entomopter velocity
Differences:Differences:
-- aerodynamic loads are complex function of wings aerodynamic loads are complex function of wings configuration andconfiguration and entomopterentomopter velocityvelocity
Open loop wing motions that can generate: Open loop wing motions that can generate: (A)(A) pitch, (B) yaw, (C) roll torques. pitch, (B) yaw, (C) roll torques.
The arrows represent theThe arrows represent the instantaneous aerodynamic forcesinstantaneous aerodynamic forces acting on the wings.acting on the wings.The circles with a cross or with a dot correspond, respectivelyThe circles with a cross or with a dot correspond, respectively to the perpendicular to the perpendicular component of the force entering or exiting the stroke plane. Advcomponent of the force entering or exiting the stroke plane. Adv. and Del. stand for . and Del. stand for advancedadvanced and delayed rotation,and delayed rotation, respectively.respectively.
Flight stabilization mechanismFlight stabilization mechanism
Flight stabilization mechanismFlight stabilization mechanism
Global pitching moment generatedby different wing pitch reversals on both stroke endsGlobal pitching moment generatedby different wing pitch reversals on both stroke ends
Flight stabilization mechanismFlight stabilization mechanism
Control in rollCControlontrol in rollin roll
Flight stabilization mechanismFlight stabilization mechanism
Control in yawCControlontrol in yawin yaw
Coordinates systemCoordinates system
Coordinates systemCoordinates system Coordinates systemCoordinates system
Location of points, radius vectors,vectors of velocities and vectors of accelerationsLocation of points, radius vectorsLocation of points, radius vectors,,vectorsvectors of velocities and of velocities and vectorsvectors of of accelerationsaccelerations
MMMAAATTTHHHEEEMMMAAATTTIIICCCAAALLL MMMOOODDDEEELLL OOOFFF FFFLLLAAAPPPPPPIIINNNGGG WWWIIINNNGGGSSS
MMMAAAVVV MMMOOOTTTIIIOOONNN
The formalism of analytical mechanics allows to present dynamic
equations of motion of mechanical systems in generalised co-ordinates,
giving incredibly interesting and comfortable tool for construction of
equation of motion of aircraft. An example can be Gibbs-Appel equations.
Those equations have the following form:
=
∂
∂S
dt
d
(1)
where: q - is the vector of generalised co-ordinates
( )tS ,,, qqq
- is so called Appel function,
or functional of accelerations
Functional S for i-th element of the mechanical system is given by the eguation:
ii
V
idmS
i
vv ∫∫∫= 2
1
(2)
where:
iv means the vector of absolute acceleration
of elementary mass dmi of i-th body
of the considered dynamical system.
and:
( )iiiiiiiρωωρεvv ××+×+= 0000
(3)
Assuming that:
iiii333 rrrr +′+′′= (4)
and:
=
iO
i
Tiiii
m
mm
Jr
rIM
~
~
(5)
+
+=
000
200
~~~
~~
ωJωvωr
rωωh
iO
iii
iiii
m
mm
(6)
where: mi - mass of the i-th element, JO
i tensor of inertia of the i-th element,
ωωωω0000 - vector of the angular velocity, v0
i - vector of the velocity of the i-th element,
[ ]Taaa ςηξ ,,=awhere if where if where if thanthanthan
0
0
0
a a
a a
a a
ς η
ς ξ
η ξ
−
= − −
a
The term (2) can be expressed in the following matrix form: The term (2) can be expressed in the following matrix form:
( ) ( )
+
+=
−− iiiiT
iiiiS hMvMhMv11
2
1
Defining matrixes for all k bodies of the system:
= kdiag MMMM ...,,..., 21
(8)
( ) ( ) ( )
TTkTT
= vvvv ...,,...,
21
(9)
( ) ( ) ( )
TTkTT
= hhhh ...,,..., 21
(10)
(7)(7)(7)
Functional S for the whole mechanical system is given by the equation:
( ) ( )hMvMhMv
11
2
1 −− ++= T
S (11)
Assuming that q is vector generalised coordinates of mechanical system, the
relations between q and v are given by equation:
( ) ( )tt ,, qfqqDv += (12)
hence:
( ) ( )tt ,,, qqφqqDv += (13)
where:
fqD +=ϕϕϕϕ
Therefore the Appel function can be expressed by following relation:
( ) ( ) ( )hqDMhqDqqq11111111 ΜΜΜΜϕϕϕϕΜΜΜΜϕϕϕϕ −− ++++=
TtS
2
1,,,
(14)
Assuming, that:
MDDM Tg = and ( )hMDh += ϕϕϕϕ
Tg
and remembering, that:
( ) 11 −−= MDMDDD TT
the equation (14) can be expressed in the form:
( ) ( ) ( )gg
T
ggtS hqMhqqqq11111111 ΜΜΜΜΜΜΜΜ
−− ++=
2
1,,,
(15)
Vector of generalised co-ordinates has following form
[ , , , , , , , , , , , ]T
s s s R L R L R Lx y z β β ζ ζ θ θ= Φ Θ Ψq (16)
vector of quasi-velocities can be expressed by the following equation
[ , , , , , , , , , , , ]T
R L R R R Lu v w p q r β β ζ ζ θ θ=w (17)
For the holonomic dynamical system the relation between generalized velocities
[ ]1 2, ,........T
nq q q=q and quasi velocities [ ]1 2 3, ,........T
w w w=w
is following:
( )T=q A q w (18)
The matrix AT has a construction:
G
T T
=
A 0 0
A 0 C 0
0 0 I (19)
From (18) we have the following relation:
( ) wAwqAq TT += (20)
Finally, the Appel function has following form:
( ) ( ) ( )www
T
wwtS hMwMhMwwwq11*
2
1,,,
−−++=
where ( ) TqTTw AMAqM = , and: ( ) ( )qTq
TTw hAMAwqh += ,
Gibbs-Appel equations of motion, written in quasivelocities has the following
form:
( ) ( ) ( )tttw
S
w
SSww
T
k
T
,,,,,..,,......... **
1
**
wqQwqhwqMw
=+=
∂
∂
∂
∂=
∂
∂
(21)
Mathematical model for Mathematical model for nonlinearnonlinear flight simulationflight simulation
( ) C CΩ Ω Ω Ω Ω+ + + + = +MV M J J J R MJ R MJ F G
( )( ) ( ) 0
2R L w w
B B R B L s S S S
R R L L
B B R B B L B C
Ω
Ω Ω Ω
+ + + + + + +
+ + + + + = + ×
J Ω J O J O J J J J J V
J J J O J J J O J J Ω M R G
where: M=mI, m – mass of MAV, I – unit matrix,
F=[Fx, F
yF
z]T– vector of aerodynamic forces,
M0=[L,M,N]T – vector of aerodynamic moments,
V=[U,V,W]T – velocity vector;
ΩΩΩΩ=[P,Q,R]T – vector of angular velocity,
- vector of right wing angular rates,
- vector of left wing angular rate;
Rc=[x
c, y
c, z
c]T vector of the center of mass
, ,T
R P Q Rβ θ = − + O
, ,T
L P Q Rβ θ = + + O
Mathematical model for Mathematical model for nonlinearnonlinear flight simulationflight simulation
SSxx,, SSyy,, SSzz- static moments static moments of of entomopterentomopter without wings; without wings;
-- matrix of static moments ofmatrix of static moments of MAVMAV’’ss wing; wing;
JJBB -- inertial moment of MAV without wings;inertial moment of MAV without wings;
-- inertial moments of right and left wing.inertial moments of right and left wing.
The control vector is defined as follows: The control vector is defined as follows:
where: β - flapping angle of wings;
φ - feathering angle of wings,
ω - frequency of wing motion respect to the body;
ψ - phase shifting between feathering and flapping;
and: ,
0
0
0
R Q
R P
Q P
− = − −
ΩJ
0
0
0
z y
S z x
y x
S S
S S
S S
−
= − −
J
w
SJ
R L
B B,J J
[ ], , ,T
=u β φ ω ψ
0 sin tβ β ω=0sin ( )t= +φ φ ω ψ
Mathematical model for nonlinear flight simulationMathematical model for nonlinear flight simulation
In those equations:In those equations:
2
0
1( ( , ), ( , )) ,
2x x L D
F V SC C Cρ α α= u u
2
0 0
1( , ),
2x l
M V SbCρ α= u
2
0
1( ( , ), ( , )) ,
2y y L DF V S C C Cρ α α= u u
2
0
1( ( , ), ( , )) ,
2z z L DF V S C C Cρ α α= u u
2
0 0
1( , ),
2y mM V SbCρ α= u
2
0 0
1( , ).
2z nM V SbCρ α= u
Distribution of instantaneous forcesDistribution of instantaneous forces aalong a long a wingbeatwingbeat cycle:cycle:
Important to stabilise the flight.
FundamentalFundamental kinematickinematic parametersparameters:
flapping angle of wingsflapping angle of wings β.β.β.β.β.β.β.β.
feathering angle feathering angle of of wingswings φφφφφφφφ
frequency of wing motion respect frequency of wing motion respect
to the bodyto the body ωωωωωωωω,,,,,,,,
phase phase shifting between feathering shifting between feathering
and flappingand flapping ψψψψψψψψ,,,,,,,,
mean strokemean stroke ηηηηηηηη, ,
angle of attack angle of attack αααααααα
Large and impulsive forces at the Large and impulsive forces at the
wing reversals.wing reversals.
Aerodynamic model - Blade Element Method (BEM) Aerodynamic model - Blade Element Method (BEM)
( ) ( ) ( )
( )( )( ) ( )( )
( )
3
3
cos cos sin sin sin cos sin sin sin cos sin cos
cos sin cos cos sin sin
sin cos cos sin sin sin sin cos sin cos cos cos cos cos
sin cos cos sin sin
ti M M M
ni M
U U V W
P Q R z
P Q R y
U U
β ζ β θ ζ β ζ β θ ζ β θ
θ ζ θ ζ θ ζ θ
β ζ β θ ζ β ζ β θ ζ β θ γ ζ θ β
β ζ β θ ζ
= + + − + − +
+ − + − − +
+ − + + + + +
= −
( )
( ) ( )( )( )
3
3
sin sin cos sin cos cos cos
cos cos sin sin sin cos sin sin sin sin sin cos cos cos sin
cos sin cos cos sin sin
M MV W
P Q R z
P Q R x
β ζ β θ ζ β θ
β ζ β θ ζ β ζ β θ ζ β θ θ β ζ θ β
β ζ θ ζ θ ζ δ
+ + + +
+ + + − − + − +
− − + − −
sin cosi Xi i Zi i
T P Pθ θ∆ = −∆ + ∆ cos sini Xi Zi i
Q P Pθ θ∆ = −∆ − ∆
θ γ α= −
21( )
2Xi i Xi i iP cU C bρ α∆ = ∆ ( )21
2Zi i Zi i iP c U C bρ α∆ = ∆
arc tan ni ii
ti
U V
Uα
−=
Modeling of aerodynamic loadsModeling of aerodynamic loads steady steady state model state model -- BEMBEM Aerodynamic model – quasi unsteady approachAerodynamic model – quasi unsteady approach
Stroke angle
Rotation angle
Lift
Wing
thrust (drag)
BEMBEM: results of simulations: results of simulations
Pressure force:
Translational
Pressure force:
Roto-translational
Neuromotor Control in Insects
compound eyes allow insects completelyto survey the surroundings|compound eyes allow insects completelyto survey the surroundings|
Flight-related sensors of the housefly Musca domestica: - the large compound eyes dominate the head;- ocelli are three light-sensitive sensors on top of the head;- halteres appear on the thorax and beat in anti-phase to the wings, sensing rotary
motion
Ocelli like light-sensitive sensors applied to stabilization of MAV flight
α
XB
yB
Z B
P1
P4
P2
P3
eb3
A 3
Four photoreceptors are fixed
with respect to the insect’s body frame
Four photoreceptors are fixed
with respect to the insect’s body frame
Promieniowanie podczerwone
fotodiody
R V1
V2
x
y
z
α
Schematics of ocelli designSchematics of ocelli design
Ocelli and halters like light-sensitive sensors applied to stabilization of MAV flight
sin cos
sin sin
cos
p
p
p
x r
y r
z r
θ ψ
θ ψ
θ
=
=
=
2 2
1 2
2 2
3 4
1 ,0, , 1 ,0,
0, 1 , , 0, 1 ,
T Tb b
T Tb b
P h h P h h
P h h P h h
= − = − −
= − = − −
1 2 1 2
( ) ( , ) ( )
( ) ( )
I P I I
I I
ψ θ θ
θ θ θ θ
= =
< ⇒ >1 1 2
2 3 4
( ) ( )
( ) ( )
a a
a a
y I P I P
y I P I P
= −
= −α
XB
yB
Z B
P1
P4
P2
P3
eb3
A 3
( )( )
( )( )1 22 2
1 33 31 33 311 1dI dI
y I hr r h I hr r hd d
ξ ξ
θ θ
= + − − − −
( ) ( )[ ] ( )[ ] ( )[ ] zyxl tmftfmtfmtF ωωαωα 211 2cos2sin2 −+−=
( ) ( )[ ] ( )[ ] ( )[ ] zyxr tmftfmtfmtF ωωαωα 211 2cos2sin2 +++=
Ocelli like light-sensitive sensors applied to stabilization of MAV flight
(Paparazzi autopilot)
Stabilization of hovering flight -Nonlinear Inverse Dynamics (NID) approach
Stabilization of hovering flight -Nonlinear Inverse Dynamics (NID) approach
( ) ( )f g= +x x x u ( )y = h x
( ) ( )-1 u = D x v - N x-1
( )
0
0
rj
z j
j=
∑v = P y - P y
1 11 1
1 1 1 1
1 1
1
h , ..., h
... ... ...
h , ..., hm m
r r
r r
m m m
L L L L
L L L L
− −
− −
=
G T G T
G T G T
D
h hL = ∇F F ( ) ( )[ h ]jr
jL= FN x x
Averaging Theory:
If forces change very rapidly relative to body If forces change very rapidly relative to body
dynamics, only dynamics, only meanmean forces and torques determineforces and torques determine
this this body body motionmotion
Averaging: system with inputs
How do we choose the T-periodic function w(v,t) ?
- Geometric control
- BIOMETICS: mimic insect wings trajectory
How can we compute ?
- For insect flight this boils down to computingmean forces and torques over a wingbeat period:
Simulations Force platform
How small must the period T of the periodic input be?
- Wingbeat period of all insects is good enough
How do we choose the THow do we choose the T--periodic function w(v,t) ?periodic function w(v,t) ?
-- Geometric control Geometric control
-- BIOMETICS:BIOMETICS: mimic insect wings trajectorymimic insect wings trajectory
How can we computeHow can we compute ??
-- For insect flight this boils down to computingFor insect flight this boils down to computingmeanmean forces and torquesforces and torques over aover a wingbeatwingbeat period:period:
Simulations Simulations Force platformForce platform
How small must the period T of the periodic input be?How small must the period T of the periodic input be?
-- WingbeatWingbeat periodperiod of all insects is good enoughof all insects is good enough
How doing it ? 3 IssuesHow doing it ? 3 Issues
( ) ( ) ( ) ( ), w ,f s S= = +x x u x x u u
Parameterization of wings motionParameterization of wings motion
( )( )( )
,w ,
,
b
a
b
a
=
F u uu u
M u u
( )
( )( ) ( ) ( ) ( ) ( )( )
( ) ( ) ( )0 0
,
1 1, , , , ,
T T
f
f g t dt s S w g t g t dtT T
s S
= =
= = + =
= +
∫ ∫
.
x x v
x v x x v v
x x w v
Parameterization of wings motion
( ) ( ) ( )( )0
1, , ,
T
w w g t g t dtT
= ∫v v v
( ) ( ) ( )1 1,t g t g tφφ = +v v ( ) ( ) ( )2 2,t g t g t= +v vηη
( )2
cos3
g t tT
φ
π π =
( )
2cos
4g t t
Tη
π π =
( )1
2cos
15g t t
T
π π =
( ) ( )2 1g t g t=
Parameterization of wings motionHovering flight stabilization - NID aproach
( ), , ,R L R L
=u φ φ η η
( ) ( ) ( ),v t t t= +0u g G v
( )1 1 2 2, , ,R L R L
v v v v=v
1
1
0
2
2
0 0 0
0 0 0,
0 0 0
0 0 0
g g
g g
g g
g g
= =
g G
φ
φ
η
η
ParametrizationParametrizationof wings motionof wings motion
Block diagram of Block diagram of DynamicsDynamics ModuleModule
Block diagram of Aerodynamic Block diagram of Aerodynamic MModuleodule Algorithm Flow Chart:Algorithm Flow Chart:
Simulation of Simulation of
hovering flighthovering flight
(uncontrolled (uncontrolled
motionmotion))
Stabilization of hovering flight (NID)
Stabilization of hovering flight (NID) Stabilization of hovering flight (NID)
Rφ
Rη
Rφ
Lη
Conclusions
Fixed wing MAV technology seems to be an almost ready solution fFixed wing MAV technology seems to be an almost ready solution for or applications requiring high airspeed and long endurance. applications requiring high airspeed and long endurance. MicrohelicoptersMicrohelicopters can supplement fixed wing designs if hovering is can supplement fixed wing designs if hovering is required, but more work has to be done to increase their enduranrequired, but more work has to be done to increase their endurancece..
CConcepts oncepts entomoptersentomopters seem to be the most promising, because their seem to be the most promising, because their successful application would provide high successful application would provide high manoeuvrability manoeuvrability and and efficiency in hover. efficiency in hover.
TThe highly nonlinear nature of the he highly nonlinear nature of the entomoptersentomopters’’ss mathematical model mathematical model caused, that caused, that the use of linear control schemesthe use of linear control schemes is rather impossibleis rather impossible. . As As solution it is possible to apply solution it is possible to apply fuzzy controllers, fuzzy controllers, oror genetic algorithms genetic algorithms and neural networksand neural networks..
The The GurGur Game, an algorithm for selfGame, an algorithm for self--optimization and selfoptimization and self--organization organization for distributed nonlinear systems, for distributed nonlinear systems, also can be applied.also can be applied.
The results demonstrate that flexible membranes improved lift anThe results demonstrate that flexible membranes improved lift and thrust d thrust performance not by maximizing the positive force peaks, but rathperformance not by maximizing the positive force peaks, but rather by er by minimizing the negative peaks.minimizing the negative peaks.
TThese performance gains arose from localized areas of the wing duhese performance gains arose from localized areas of the wing during ring very short time periods of the overall flapping cycle. The physivery short time periods of the overall flapping cycle. The physical cal mechanism for these gains was that wing deformation due to incremechanism for these gains was that wing deformation due to increased ased flexibility caused a favorable tilting of the overall force vectflexibility caused a favorable tilting of the overall force vector, thereby or, thereby reducing the negative force componentsreducing the negative force components
… or alternative solution …„Computer Electronics Meet Animal Brains”… or alternative solution …„Computer ElectronicsComputer Electronics Meet Animal BrainsMeet Animal Brains””
COMPUTER, January, 2003;
Published by the IEEE Computer Society © 2003 IEEE
COMPUTERCOMPUTER, , JanuaryJanuary,, 20032003;;
Published by the IEEE Computer Society Published by the IEEE Computer Society ©© 2003 IEEE2003 IEEE
Chris Diorio
Jaideep Mavoori
University of Washington
Chris Chris DiorioDiorio
Jaideep MavooriJaideep Mavoori
UniversityUniversity of Washingtonof Washington
Computer Electronics Meet Animal BrainsComputer ElectronicsComputer Electronics Meet Animal BrainsMeet Animal Brains
Neurochip functional block diagram. Solid lines show required components,dashed lines show some optional components
NNeurochipeurochip functional block diagram.functional block diagram. Solid lines show required components,Solid lines show required components,
dashed lines show some optional componentsdashed lines show some optional components
ComputerElectronicsMeetAnimal Brains
ComputerComputerElectronicsElectronicsMeetMeetAnimal BrainsAnimal Brains
Integrative biology using neurochips. A neurochip can splice into neuromuscular pathways to unravel the internal biological circuitry.Neurochips armed with recording and stimulation channels will help neurobiologists understand the complex interactions amongthemoth’s various neural control systems.