Intelligent Bi-State Control for the Structure with Magnetorheological Dampers

Abstract

Magnetorheological (MR) damper is a kind of intelligent control device, which can be used to reduce earthquake responses of building structures. In this paper, a mathematical model of MR dampers is introduced, and the relation between the yielding shear stress and the control current of MR dampers is formulated. Then the bi-state control strategy of the structure with MR dampers is studied. Results show that the control strategy easily leads to overrun of the parameters and the control force of MR dampers under the minor earthquake, which results in amplification of acceleration responses of the structure. For this reason, a modified bi-state control strategy is proposed. In this method, the neural network technique is used to predict the seismic responses of the structure, whilst the intelligent bi-state control strategy on the smart structure with MR dampers is applied to solve the time-delay problem of semiactive control. Numerical simulation of a three-story reinforced concrete frame structure with MR dampers and without MR dampers are performed using the bi-state control method, the modified bi-state control method and the intelligent bi-state control method. Results show that the intelligent control strategy solves the time-delay problem of semiactive control and overrun of control forces under the minor earthquake, and the control force is most veritable and the earthquake mitigation effect is the best.

Get full access to this article

View all access options for this article.

References

Carlson, J. D. and Spencer, B. F. 1996a. “Magnetorheological Fluid Dampers: Scalability and Design Issues for Application to Dynamic Hazard Mitigation,” In: Proceedings of the 2nd International Workshop on Structural Control , pp. 99 - 109 , HongKong .

Carlson, J. D. and Spencer, B. F. Jr. 1996b. Magneto-rheological Fluid Dampers for Semi-active Seismic Control,” In: Proc. of 3rd Int. Conf. on Motion and Vibration Control , pp. 35 - 40 , Japan Society of Mechanical Engineers, Tokyo .

Carlson, J. D. and Weiss, K. D. 1994 . “A Growing Attraction to Magnetic Fluids,” Machine Design , 66 ( 15 ): 61 - 64 .

Dyke, S. J. , Spencer, B. F., Jr. , Sain, M. K. and Carlson, J. D. 1996 . “Modeling and Control of Magnetorheological Dampers for Seismic Response Reduction,” Smart Materials and Structure , 5 : 565 - 575 .

Fujita, T. , Jeyadevan, B. , Yamaguchi, K. and Nishiyama, H. 1999 . “Preparation, Viscosity and Damping of Functional Fluids that Respond to Both Magnetic and Electric Fields,” Powder Technology , 101 : 279 - 287 .

Gavin, G. P. , Hanson, R. D. and Filisko, F. E. 1996 . “Electrorheological Dampers, Part I: Analysis and Design,” Journal of Applied Mechanics , 63 : 669 - 675 .

Hagan, M. T. and Menhaj, M. B. 1994 . “Training Feedforward Networks with the Marquardt Algorithm,” IEEE Trans. of Neural Networks , 5 ( 6 ): 989 - 993 .

Jansen, L. M. and Dyke, S. J. 2000 . “Semiactive Control Strategies for MR Dampers: Comparative Study,” Journal of Engineering Mechanics, ASCE , 126 ( 8 ): 795 - 803 .

Jolly, M. R. , Bender, J. W. and Carlson, J. D. 1999 . “Properties and Applications of Commercial Magnetorheological Fluids,” Journal of Intelligent Material Systems and Structures , 10 : 5 - 13 .

10.

Kamagata, S. and Kobori, T. 1994. “Autonomous Adaptive Control of Active Variable Stiffness Systems for Seismic Ground Motion,” In: Proceedings, 1st World Conference on Structural Control , Vol.2, USC Publications, Los Angeles , TA 4-33.

11.

Kamath, G. M. and Werely, N. M. 1997 . “A Nonlinear Viscoelastic-plastic Model for Electrorheological Fluids,” Smart Materials and Structure , 6 : 351 - 359 .

12.

Li, W. H. , Yao, G. Z. , Chen, G. , Yeo, S. H. and Yap, F. F. 2000 . “Testing and Steady State Modeling of a Linear MR Damper under Sinusoidal Loading,” Smart Materials and Structure , 9 : 95 - 102 .

13.

Liu, G. P. and Daley, S. 1999 . “Output-model-based Predictive Control of Unstable Combustion Systems Using Neural Networks,” Control Engineering Practice , 7 ( 5 ): 591 - 600 .

14.

McClamroch, N. M. and Gavin, H. P. 1995. “Closed Loop Structural Control Using Electrorheological Dampers,” In: Proceedings of American Control Conference , pp. 4173 - 4177 , American Automatic Control Council, Washington, D.C.

15.

Nikzad, K. , Ghaboussi, J. and Stanley, L. P. 1996 . “Actuator Dynamics and Delay Compensation Using Neurocontrollers,” Journal of Engineering Mechanics, ASCE , 122 ( 10 ): 966 - 975 .

16.

Ou, J. P. and Guan, X. C. 1999 . “Experimental Study of MR Damper Performance,” Earthquake Engineering & Engineering Vibration , 19 ( 4 ): 76 - 81 .

17.

Phillips, R. W. 1969. Engineering Applications of Fluids with a Variable Yield Stress, Ph.D. Thesis, University of California, Berkeley.

18.

Ray, W. C. and Josoph, P. 1975 . “Dynamics of Structures,” McGraw-Hill , New York .

19.

Shahram, S. , Loren, D. L. and Shuang, J. 1999. “Simplified Algorithm for Seismic Control of Civil Structures Using Smart Dampers,” The SPIE Conference on Smart Systems for Bridges, Structures, and Highways Newport Beach , SPIE, California , 3671: 56 - 63 .

20.

Spencer, B. F. , Dyke, S. J. , Sain, M. K. and Carlson, J. D. 1997 . “Phenomenological Model for Magnetorheological Dampers,” Journal of Engineering Mechanics, ASCE , 123 ( 3 ): 230 - 238 .

21.

Tan, Y. and Sain, M. 2000 . “Neural-networks-based Nonlinear Dynamic Modeling for Automotive Engines,” Neurocomputing , 30 : 129 - 142 .

22.

Vicken, K. and Celal, B. 1997 . “Model Reference Based Neural Network Adaptive Controller,” ISA Transactions , 37 : 21 - 39 .

23.

Werely, N. M. , Pang, L. and Kamath, G. M. 1998 . “Idealized Hysteresis Modeling of Electrorheological Dampers,” Journal of Intelligent Material Systems and Structures , 9 ( 8 ): 642 - 649 .

24.

Xu, Y. L. , Qu, W. L. and Ko, J. M. 2000 . “Seismic Response Control of Frame Structure Using Magnetorheological/Electrorheological Dampers,” Earthquake Engineering and Structure Dynamics , 29 : 557 - 575 .

25.

Xu, Z. D. 2002 . Study on Semi-active Control of Structures Incorporated with Magnetorheological Dampers , Report of Postdoctoral Fellowship, Xi'an Jiaotong University, Xi'an , China .

26.

Zeidenberg, M. 1990 . “Neural Network Models in Artificial Intelligence,” E.Horwood , New York .)