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

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

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

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

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

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

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

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

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

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  Analysis integration time : sec sampling time : sec time delay : sec  Neural Network

19  Learning

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

21 Northridge(1994)

22  Transfer function( )

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

24  Control results(nonlinear) controlled uncontrolled

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.