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


• 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


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


• 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


• Objective• Objective

• To propose a new semiactive control method using MR damper for seismically excited structures in conjunction with a neural network algorithm


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.


STRUCTURE

Neural Network

xx ,gx

fMR Damper

Clipped

Algorithmdf

v

Clipped Neuro-Control

Block diagram of the proposed algorithm


• 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


)(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:


• 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


• 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


: 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.


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


• Neural network used in the numerical example• Neural network used in the numerical example

1x

3x

1x

3x

gx

df

input output


• 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.


• 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


• 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


• 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


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.


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.


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 !!!


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




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 clipped optimal algorithm is more sensitive than proposed

algorithm to the different frequency components of the earthquake !!!


0 0.2 0.4 0.6 0.80.75

1

1.25

1.5

1.75

2

Clipped optimalProposed algorithm

Kobe earthquake



• Maximum drift of 3rd floor (Normalized by those of active neuro-control algorithm)

Active neuro-control


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.


0 0.2 0.4 0.6 0.80.5

1

1.5

2

2.5

3

3.5


Kobe earthquake



• Maximum acceleration of 1st floor (Normalized by those of active neuro-control algorithm)


Nor

mal

ized

Max

imu

m d

rift

of

3rd f

loor


• Maximum acceleration often occurs at 1st floor.

• Proposed algorithm shows the best performance

among the three algorithm.


0 0.2 0.4 0.6 0.80.5

0.6

0.7

0.8

0.9

1


Kobe earthquake



• Maximum control force (Normalized by those of active neuro-control algorithm)


Nor

mal

ized

Max

imu

m d

rift

of

3rd f

loor


• Proposed algorithm needs less control force than the others. • Proposed algorithm shows a better performance than the other conventional algorithms with less control force!!!


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.


The proposed semiactive neuro-control technique using MR dampers could be effectively used for control of seismically excited structures!


Thank you for your attention.Thank you for your attention.Thank you for your attention.Thank you for your attention.

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Semiactive Neuro-Control for Seismically Excited Structure Using MR Damper