Intelligent Control of Structures under Earthquakes

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

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

6. 2. NEURAL NETWORKS FOR CONTROL • 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. Two Neural Networks

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

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

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

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

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

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

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

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

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

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

17. 4. NUMERICAL EXAMPLES  Model(linear) • Structure mass : 200kg(story) stiffness : k0=2.25105N/m(inter-story) damping : 0.6, 0.7, 0.3% for each mode • AMD mass : 3% of total mass(18kg) stiffness :optimal stiffness for TMD ( ) damping :optimal damping for TMD ( )

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

19.  Learning

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

21. Northridge(1994)

22.  Transfer function( )

23.  Model(nonlinear) • Structure mass, damping : the same as linear model stiffness : =2.25105N/m(inter-story), =0.5

24.  Control results(nonlinear) uncontrolled uncontrolled controlled controlled <El Centro earthquake> <Northridge earthquake>

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.