1. 반능동 MR 유체 감쇠기를 이용한 지진하중을 받는 구조물의 신경망제어 이헌재, 한국과학기술원 건설환경공학과 석사과정
한국전산구조공학회 추계 학술발표회
원광대학교, 익산
2002년 10월 19일
반능동 MR 유체 감쇠기를 이용한
지진하중을 받는 구조물의 신경망제어
이헌재, 한국과학기술원 건설환경공학과 석사과정
정형조, 한국과학기술원 건설환경공학과 연구조교수
오주원, 한남대학교 토목공학과 교수
이인원, 한국과학기술원 건설환경공학과 교수

2.  OUTLINE OF THE NEURAL NETWORK  ACTIVE CONTROL SYSTEM USING N. N.
CONTENTS
 INTRODUCTION
 OUTLINE OF THE NEURAL NETWORK
 ACTIVE CONTROL SYSTEM USING N. N.
 SEMIACTIVE CONTROL SYSTEM USING N. N.
 NUMERICAL EXAMPLE
 CONCLUSIONS
Structural Dynamics & Vibration Control Lab., KAIST, Korea

3. INTRODUCTION Problems Solutions
Vibration control of seismically excited structure using artificial neural network was proposed by Ghaboussi et al. (1995) and Chen et al. (1995)
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
Structural Dynamics & Vibration Control Lab., KAIST, Korea

4. Active control systems still have potential to destabilize the structural system.
Semiactive control systems not only offer the reliability of passive control systems but also maintain the versatility and adaptability of fully active control systems.
기존의 신경망을 이용한 진동제어들은 능동제어시스템이었다.
Structural Dynamics & Vibration Control Lab., KAIST, Korea

5. Outline of the Neural Network… … …
: activation function
: activation function
Structural Dynamics & Vibration Control Lab., KAIST, Korea

6. Neural network using cost function (Kim et al., 2000)
: state vector
: control signal
: weighting matrix
: sampling number
: total number of sampling times
Structural Dynamics & Vibration Control Lab., KAIST, Korea

7. By applying the gradient decent rule to the cost at k-th step, the update for the weight can be expressed as
: training rate
Using the chain rule, the partial derivative of Eq. (6) can be rewritten as
Structural Dynamics & Vibration Control Lab., KAIST, Korea

8. Let’s define the generalized error as
Finally, the weight update can be simply expressed as
In Eq. (9), the gain factor, , satisfies
The bias is also updated by
Structural Dynamics & Vibration Control Lab., KAIST, Korea

9. In the same manner, updates for the weight and bias between input layer and hidden layer can be obtained as
Structural Dynamics & Vibration Control Lab., KAIST, Korea

10. Active Control System using N. N.
Training rule
(Sensitivity Evaluation Algorithm)
CONTROLLER
STRUCTURE
(Neural network)
Earthquake
signal flow for control
signal flow for training
Structural Dynamics & Vibration Control Lab., KAIST, Korea

11. Semiactive Control System using N. N.
Training rule
(Sensitivity Evaluation Algorithm)
Secondary (Clipped algorithm)
: desired control force
: control force of MR damper
: velocity across the damper
CONTROLLER
STRUCTURE
Primary
Secondary
(Neural Network)
(Clipped algorithm)
Earthquake
signal flow for control
signal flow for training
Structural Dynamics & Vibration Control Lab., KAIST, Korea

12. NUMERICAL EXAMPLE Three-story building structure Dyke el al. (1996)
Structural Dynamics & Vibration Control Lab., KAIST, Korea

13. Neural Network Control System
displacement
of 1st floor
of 3rd floor
velocity
of 1st floor
of 3rd floor
control
force
ground
acceleration
Structural Dynamics & Vibration Control Lab., KAIST, Korea

14. Training and verification of neural network
Time(sec)
Acceleration (m/sec2)
El Centro
(0.348g)
Time(sec)
Acceleration (m/sec2)
Northridge
(0.334g)
Time(sec)
Acceleration (m/sec2)
California
(0.156g)
Structural Dynamics & Vibration Control Lab., KAIST, Korea

15. Training neural network
epoch
cost function
active
control
semiactive
the change of the cost function
Structural Dynamics & Vibration Control Lab., KAIST, Korea

16. Displacement of 3rd floor (El Centro)
Time(sec)
Displacement (cm)
Time(sec)
Displacement (cm)
Structural Dynamics & Vibration Control Lab., KAIST, Korea

17. Acceleration of 3rd floor (El Centro)
Time(sec)
Acceleration (cm/sec2)
Time(sec)
Acceleration (cm/sec2)
Structural Dynamics & Vibration Control Lab., KAIST, Korea

18. Acceleration (cm/sec2)
Displacement of 1st and 2nd floor (El Centro)
Time(sec)
Displacement (cm)
Acceleration of 1st and 2nd floor (El Centro)
Time(sec)
Acceleration (cm/sec2)
Structural Dynamics & Vibration Control Lab., KAIST, Korea

19. Verification of neural network’s performance
Displacement (cm)
Time(sec)
Acceleration
(cm/sec2)
Time(sec)
3rd floor, Northridge earthquake
Structural Dynamics & Vibration Control Lab., KAIST, Korea

20. 3rd floor, California earthquake
Displacement (cm)
Time(sec)
Acceleration
(cm/sec2)
Time(sec)
3rd floor, California earthquake
Structural Dynamics & Vibration Control Lab., KAIST, Korea

21. Results of control (El Centro earthquake)
Active
Semiactive
Uncontrolled
neuro-control
neuro-control
0.549(1.00)
0.836(1.00)
0.973(1.00)
0.098(0.18)
0.133(0.16)
0.215(0.22)
0.138(0.25)
0.198(0.24)
0.216(0.22)
(cm)
0.549(1.00)
0.317(1.00)
0.203(1.00)
0.098(0.18)
0.124(0.39)
0.082(0.40)
0.138(0.25)
0.136(0.43)
0.092(0.45)
(cm)
880(1.00)
1065(1.00)
1414(1.00)
660(0.75)
569(0.53)
567(0.40)
777(0.88)
887(0.83)
639(0.45)
(cm/sec2)
(N) -
1000
803
Structural Dynamics & Vibration Control Lab., KAIST, Korea

22. Results of control (Northridge earthquake)
Active
Semiactive
Uncontrolled
neuro-control
neuro-control
0.683(1.00)
1.102(1.00)
1.343(1.00)
0.093(0.14)
0.174(0.16)
0.210(0.16)
0.177(0.26)
0.254(0.23)
0.273(0.20)
(cm)
0.683(1.00)
0.422(1.00)
0.248(1.00)
0.093(0.14)
0.108(0.26)
0.095(0.38)
0.177(0.26)
0.129(0.39)
0.084(0.34)
(cm)
917(1.00)
1287(1.00)
1726(1.00)
661(0.72)
593(0.46)
659(0.38)
693(0.76)
837(0.65)
587(0.34)
(cm/sec2)
(N) -
1000
790
Structural Dynamics & Vibration Control Lab., KAIST, Korea

23. Results of control (California earthquake)
Active
Semiactive
Uncontrolled
neuro-control
neuro-control
0.316(1.00)
0.476(1.00)
0.544(1.00)
0.043(0.14)
0.051(0.11)
0.081(0.20)
0.069(0.22)
0.084(0.18)
0.099(0.18)
(cm)
0.316(1.00)
0.169(1.00)
0.104(1.00)
0.043(0.14)
0.046(0.27)
0.041(0.39)
0.069(0.22)
0.060(0.36)
0.042(0.40)
(cm)
581(1.00)
662(1.00)
726(1.00)
358(0.62)
270(0.41)
286(0.39)
429(0.74)
515(0.78)
291(0.40)
(cm/sec2)
(N) -
522
537
Structural Dynamics & Vibration Control Lab., KAIST, Korea

24. CONCLUSIONS
A semiactive neuro-control method using MR fluid damper is presented for seismic response reduction.
The training algorithm based on a cost function and the sensitivity evaluation algorithm are used in neuro-control algorithm.
The proposed semi-active neuro-control algorithm is quite effective to reduce seismic responses.
And, the semi-active control system has many attractive features, such as the bounded-input, bounded-output stability and small energy requirements, ant so on.
Structural Dynamics & Vibration Control Lab., KAIST, Korea

25. Thank you for your attention!
The proposed semi-active neuro-control strategy using MR fluid dampers could be effectively used for control of seismically excited structures.
Thank you for your attention!
Structural Dynamics & Vibration Control Lab., KAIST, Korea