Semiactive Neuro-Control for Seismically Excited Structure Using MR Damper
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Transcript of Semiactive Neuro-Control for Seismically Excited Structure Using MR Damper
Semiactive Neuro-Control
for Seismically Excited Structure
Using MR Damper
Semiactive Neuro-Control
for Seismically Excited Structure
Using MR Damper
Heon-Jae Lee*: Graduate StudentGraduate Student, KAIST, Korea
Hyung-Jo Jung: Professor, Sejong University, Korea
Nguyen Xuan Thanh: Graduate StudentGraduate Student, KAIST, Korea
Sun-Kyu Pakr: Professor, Sungkyunkwan University, Korea
In-Won Lee: Professor, KAIST, Korea
EASEC-9, Bali, IndonesiaEASEC-9, Bali, Indonesia
16-18, December, 200316-18, December, 2003
22Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea
IntroductionIntroduction
Proposed Semiactive Control AlgorithmProposed Semiactive Control Algorithm
Numerical ExampleNumerical Example
ConclusionConclusion
CONTENTSCONTENTS
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• Vibration control of seismically excited structure using artificial neural network was proposed by Ghaboussi et al. (1995) and Chen et al. (1995).
• Neuro-controllers do not need mathematical models and
can be said to be robust controllers.
• There are some problems with training neural network.
Introduction
• Backgrounds• Backgrounds
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Predetermining the Desired Response
Need of Emulator Neural Network
Problems
New Training Algorithm using Cost Function
Sensitivity Evaluation Algorithm
Kim et al. (2000, 2001)
Solutions
55Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea
• Semiactive Control Systems• Semiactive Control Systems
• not only offer the reliability of passive control systems but also maintain the versatility and adaptability of fully active control system.
• Clipped optimal algorithm • Representative algorithm for semiactive control system• Proposed by Dyke et al. (1996) • Device: MR damper• Combination of LQG and clipped algorithm
• Representative algorithm for semiactive control system• Proposed by Dyke et al. (1996) • Device: MR damper• Combination of LQG and clipped algorithm
66Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea
• Objective• Objective
• To propose a new semiactive control method using MR damper for seismically excited structures in conjunction with a neural network algorithm
77Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea
Proposed Semiactive Control Algorithm
• Clipped neuro-algorithm
• New efficient algorithm for semiactive control system
• Device: MR damper
• Combination of neural network and clipped algorithm
• Neural network does not require any mathematical model of the structure.
• New efficient algorithm for semiactive control system
• Device: MR damper
• Combination of neural network and clipped algorithm
• Neural network does not require any mathematical model of the structure.
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STRUCTURE
Neural Network
xx ,gx
fMR Damper
Clipped
Algorithmdf
v
Clipped Neuro-Control
Block diagram of the proposed algorithm
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• Control device: MR damper• Control device: MR damper
xy
1c
1k 0c
0k
WenBouc
Modified Bouc-Wen model
(Spencer et al., 199
6)
F
Schematic of
MR damper
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)(z)(zzz1
yxAyxyxnn
(1)
)(z1
00
10
yxkxccc
y
(2)
)( 011 xxkycF
(3)
(4)uba uccc
ba 111
ucccba 000
vuu
(5)
(6)
(7)
Governing equations of modified Bouc-Wen model:
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• Clipped algorithm• Clipped algorithm• desired force (by neural network) :desired force (by neural network) :
• generated force (by MR damper) :generated force (by MR damper) :
df
f
df
f
0v
0v
0v
0v
maxVv
maxVv
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• Control algorithm: neural network• Control algorithm: neural network
1I
2I
1nI 23no
22o
21o
1ihW
2jiW
• Outline of the neural network• Outline of the neural network
Inputlayer
Hiddenlayer
Outputlayer
1313Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea
: state vector
1
0
1
011
ˆ21
RuuQzz21ˆ ff N
kk
N
kk
T
kk
T
k JJ
zu
RQ,
: control signal
: weighting matrix
(8)
The neuro-controller is trained by minimizing the cos
t function, .
• Training algorithm (Kim et al., 2000)• Training algorithm (Kim et al., 2000)
J
• If the neuro-controller is trained by minimizing the cost function, there is no need to predetermining the desired response.
• If the neuro-controller is trained by minimizing the cost function, there is no need to predetermining the desired response.
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Numerical Example
• Three-story building structure (Dyke et al., 1996)• Three-story building structure (Dyke et al., 1996)
gx
1x
2x
3x3m
2m
1m
33 ,kc
22 ,kc
11 ,kc
kg3.98321 mmm
N/m1016.5 51 k
N/m1084.6 532 kk
sec/mN1251 c
sec/mN5032 cc
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• Neural network used in the numerical example• Neural network used in the numerical example
1x
3x
1x
3x
gx
df
input output
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• Procedure of numerical analysis • Procedure of numerical analysis
• Training
• Earthquake
a part of NS component of the 1940 El Centro
earthquake ( 0 ~ 3 sec)
(PGA : 0.348 g)
• The cost function is minimized during the training.
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• Verification 1
• After the neuro-controller is sufficiently trained, the
model is controlled by the trained neuro-controller under
the three earthquake records.
• The whole El Centro earthquake
• Kobe earthquake
• California earthquake
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• Verification 2
• To investigate the relationship between the magnitude of
earthquake and the control performance, simulations are
also conducted with several scaled earthquakes.
• The whole El Centro earthquake (50%, 200% scaled)
• Kobe earthquake (25%, 50% scaled)
• California earthquake (200%, 300% scaled)
Peak Ground Acceleration (g)
Kobe earthquake
California earthquake
El Centro earthquake
0.2 0.4 0.6 0.8 1.0
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• Control algorithms• Control algorithms
• Proposed algorithm
• Clipped optimal algorithm (Dyke et al., 1996)
• Performance comparisons• Performance comparisons
• maximum displacement
• maximum drift
• maximum acceleration
• maximum control force
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Control
Strategy
Active
neuro
Clipped optimal
Proposed
Algorithm
0.179 0.213 0.216
0.114 0.225 0.207
0.173 0.228 0.249
0.179 0.213 0.216
0.334 0.281 0.347
0.368 0.502 0.408
0.538 0.851 0.498
0.456 0.717 0.484
0.366 0.503 0.408
• The ratio of the peak responses for each controller to those of uncontrolled system under El Centro earthquake• The ratio of the peak responses for each controller to those of uncontrolled system under El Centro earthquake
aix
id
ix)(cm
)(cm
)/( 2scm
• The performance of the clipped optimal algorithm is slightly better
than that of proposed algorithm in reducing displacements and
inter-story drift.
• The absolute acceleration of the clipped optimal algorithm is larger
than that of the proposed controller.
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Control
Strategy
Active
neuro
Clipped optimal
Proposed
Algorithm
0.185 0.196 0.214
0.116 0.213 0.206
0.177 0.245 0.266
0.185 0.196 0.214
0.338 0.325 0.400
0.366 0.505 0.465
0.579 1.047 0.600
0.465 0.683 0.538
0.369 0.509 0.471
• The ratio of the peak responses for each controller to those of uncontrolled system under 50% scaled El Centro earthquake• The ratio of the peak responses for each controller to those of uncontrolled system under 50% scaled El Centro earthquake
aix
id
ix)(cm
)(cm
)/( 2scm
• It is similar to those of El Centro earthquake.
• But the 1st floor acceleration of the clipped optimal algorithm is
greater than that of uncontrolled system.
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Control
Strategy
Active
neuro
Clipped optimal
Proposed
Algorithm
0.181 0.255 0.226
0.122 0.273 0.228
0.173 0.301 0.266
0.181 0.255 0.226
0.313 0.275 0.319
0.356 0.438 0.381
0.562 0.719 0.455
0.414 0.554 0.402
0.355 0.436 0.381
• The ratio of the peak responses for each controller to those of uncontrolled system under 200% scaled El Centro earthquake• The ratio of the peak responses for each controller to those of uncontrolled system under 200% scaled El Centro earthquake
aix
id
ix)(cm
)(cm
)/( 2scm
• The performance of the proposed algorithm is better than that of
clipped optimal algorithm.
• The clipped optimal algorithm is more sensitive than proposed
algorithm to the change of the magnitude of earthquake !!!
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Control
Strategy
Active
neuro
Clipped optimal
Proposed
Algorithm
0.198 0.471 0.323
0.180 0.498 0.345
0.193 0.502 0.352
0.198 0.471 0.323
0.313 0.544 0.417
0.339 0.621 0.394
0.492 0.828 0.402
0.381 0.691 0.515
0.339 0.620 0.393
• The ratio of the peak responses for each controller to those of uncontrolled system under Kobe earthquake• The ratio of the peak responses for each controller to those of uncontrolled system under Kobe earthquake
aix
id
ix)(cm
)(cm
)/( 2scm
• The performance of the proposed algorithm is better than that of
clipped optimal algorithm.
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Control
Strategy
Active
Neuro
Clipped optimal
Proposed
Algorithm
0.137 0.172 0.137
0.094 0.196 0.148
0.120 0.198 0.174
0.137 0.172 0.137
0.235 0.229 0.268
0.336 0.383 0.364
0.423 0.683 0.318
0.354 0.436 0.383
0.335 0.383 0.367
• The ratio of the peak responses for each controller to those of uncontrolled system under California earthquake• The ratio of the peak responses for each controller to those of uncontrolled system under California earthquake
aix
id
ix)(cm
)(cm
)/( 2scm
• The performance of the proposed algorithm is better than that of
clipped optimal algorithm.
• The clipped optimal algorithm is more sensitive than proposed
algorithm to the different frequency components of the earthquake !!!
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0 0.2 0.4 0.6 0.80.75
1
1.25
1.5
1.75
2
Clipped optimalProposed algorithm
Kobe earthquake
California earthquake
El Centro earthquake
• Maximum drift of 3rd floor (Normalized by those of active neuro-control algorithm)
Active neuro-control
Peak Ground Acceleration (g)
Nor
mal
ized
Max
imu
m d
rift
of
3rd f
loor
• Maximum interstory drift often occurs at 3rd floor.
• Proposed algorithm shows a better performance
than clipped optimal algorithm for all cases.
2626Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea
0 0.2 0.4 0.6 0.80.5
1
1.5
2
2.5
3
3.5
Clipped optimalProposed algorithm
Kobe earthquake
California earthquake
El Centro earthquake
• Maximum acceleration of 1st floor (Normalized by those of active neuro-control algorithm)
Active neuro-control
Nor
mal
ized
Max
imu
m d
rift
of
3rd f
loor
Peak Ground Acceleration (g)
• Maximum acceleration often occurs at 1st floor.
• Proposed algorithm shows the best performance
among the three algorithm.
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0 0.2 0.4 0.6 0.80.5
0.6
0.7
0.8
0.9
1
Clipped optimalProposed algorithm
Kobe earthquake
California earthquake
El Centro earthquake
• Maximum control force (Normalized by those of active neuro-control algorithm)
Active neuro-control
Nor
mal
ized
Max
imu
m d
rift
of
3rd f
loor
Peak Ground Acceleration (g)
• Proposed algorithm needs less control force than the others. • Proposed algorithm shows a better performance than the other conventional algorithms with less control force!!!
2828Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea
Conclusions
• A semiactive neuro-control technique using MR damper for seismically excited structure is proposed.
• The clipped optimal algorithm is more sensitive than proposed algorithm to the change of the magnitude and the different frequency components of earthquake.
• Proposed algorithm shows a better performance than the other conventional algorithms with less control force.
2929Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea
The proposed semiactive neuro-control technique using MR dampers could be effectively used for control of seismically excited structures!
3030Structural Dynamics & Vibration Control Lab., KAIST, KoreaStructural Dynamics & Vibration Control Lab., KAIST, Korea
Thank you for your attention.Thank you for your attention.Thank you for your attention.Thank you for your attention.