1. 1 지진시 구조물의 지능제어 기법 Intelligent Control of Structures under Earthquakes 김동현 : 한국과학기술원 토목공학과, 박사과정 이규원 : 전북대학교 토목공학과, 교수 이종헌 : 경일대학교 토목공학과, 교수 이인원 : 한국과학기술원 토목공학과, 교수

2. 2 CONTENTS 1. INTRODUCTION 2. NEURAL NETWORKS FOR CONTROL 3. STRUCTURE WITH AMD 4. NUMERICAL EXAMPLES 5. CONCLUSIONS

3. 3 1. INTRODUCTION required impossible/hard Response based ANN control Model based conventional control Mathematical model Parametric uncertainty Nonlinearity not required simple/easy  Conventional Control vs. ANN Control

4. 4  Previous Works on ANN Control in CE H. M. Chen et al. (1995), J. Ghaboussi et al. (1995) - pioneering research in civil engineering K. Nikzad (1996) - delay compensation K. Bani-Hani et al. (1998) - nonlinear structural control J. T. Kim et al. (2000) - optimal control using neural network

5. 5 Training rule of controller neural network MDOF linear/nonlinear structural control Actuator dynamics and time delay effects are trained  Scope

6. 6 Emulator neural network - trained to imitate responses of unknown structures. - used for obtaining the sensitivity of response to control force Controller neural network - trained to make control force. - used for controller. 2. NEURAL NETWORKS FOR CONTROL  Two Neural Networks

7. 7 Controller (ANN) Minimize error(E) Emulator (ANN) Structure Load Z -1 + _ D (desired response)  E=D-X  Previous Studies Weights of controller neural network are updated to minimize error function(E). U X

8. 8 Controller (ANN) Minimize cost(J) Emulator (ANN) Structure Load Z -1  Proposed Method Weights of controller neural network are updated to minimize cost function(J) instead of error function(E). U X

9. 9 (1) : response vector at t=kT Cost function : relative weighting matrices  Learning Rule : sampling time : control force vector at t=kT

10. 10 Controller neural network l –th layer(l+1)-th layer... Input layer Output layer …... … (2) (3) Output at (l+1)th layer

11. 11 define Weight Learning rule Bias Learning rule (4) (5) (6) (7)

12. 12 are evaluated at t=kT (8) is obtained from the emulator neural network  

13. 13 3. STRUCTURE WITH AMD  Structure : mass matrix : damping matrix : stiffness vector : actuator location vector : displacement vector : ground acceleration : control force (9)

14. 14 Nonlinear model(Bouce-Wen, 1981) (10) inter-story restoring force (11) : percentage linearity : linear stiffness where

15. 15  AMD(Active Mass Driver) (12) (13) : oil flow rate : electric signal(volt) : relative velocity between the added mass and the roof Valve : Cylinder :

16. 16 kT(k+1)T detect x u k-1 Control signal delayed time compute u k ZOH ukuk Time  Control time delay

17. 17 4. NUMERICAL EXAMPLES mass : 200kg(story) stiffness : k 0 =2.25  10 5 N/m(inter-story) damping : 0.6, 0.7, 0.3% for each mode mass : 3% of total mass(18kg) stiffness :optimal stiffness for TMD ( ) damping :optimal damping for TMD ( )  Model(linear) Structure AMD

18. 18  Analysis integration time : 0.0005 sec sampling time : 0.005 sec time delay : 0.0005 sec  Neural Network

19. 19  Learning

20. 20  Control results(linear) El Centro(1940)

21. 21 Northridge(1994)

22. 22  Transfer function( )

23. 23 mass, damping : the same as linear model stiffness : =2.25  10 5 N/m(inter-story), =0.5  Model(nonlinear) Structure

24. 24  Control results(nonlinear) controlled uncontrolled

25. 25 4. CONCLUSIONS Learning rule of neural network for optimal control is proposed. Actuator dynamics and time delay effect is included in the learning Nonlinear three-story structure is controlled successfully.