Application of Support Vector Machine-Based Semiactive Control for Seismic Protection of Structures with Magnetorheological Dampers

Based on recent research by Li and Liu in 2011, this paper proposes the application of support vector machine- (SVM-) based semiactive control methodology for seismic protection of structures with magnetorheological (MR) dampers. An important and challenging task of designing the MR dampers is to develop an effective semiactive control strategy that can fully exploit the capabilities of MR dampers. However, amplification of the local acceleration response of structures exists in the widely used semiactive control strategies, namely “Switch” control strategies. Then the SVM-based semiactive control strategy has been employed to design MR dampers. Firstly, the LQR controller for the numerical model of a multistory structure formulated using the dynamic dense method is constructed by using the classic LQR control theory. Secondly, an SVM model which comprises the observers and controllers in the control system is designed and trained to emulate the performance of the LQR controller. Finally, an online autofeedback semiactive control strategy is developed by resorting to SVM and then used for designing MR dampers. Simulation results show that the MR dampers utilizing the SVM-based semiactive control algorithm, which eliminates the local acceleration amplification phenomenon, can remarkably reduce the displacement, velocity, and acceleration responses of the structure.

[1]  Francis Eng Hock Tay,et al.  Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[2]  Sami F. Masri,et al.  Modeling the oscillatory dynamic behaviour of electrorheological materials in shear , 1992 .

[3]  F. Ikhouane,et al.  Systems with Hysteresis: Analysis, Identification and Control Using the Bouc-Wen Model , 2007 .

[4]  Jinping Ou,et al.  Design approaches for active, semi-active and passive control systems based on analysis of characteristics of active control force , 2009 .

[5]  Celestino Ordóñez,et al.  Creating a quality map of a slate deposit using support vector machines , 2007 .

[6]  Qiang Zhou,et al.  Intelligent control for braking-induced longitudinal vibration responses of floating-type railway bridges , 2009 .

[7]  Rong Chen,et al.  Online weighted LS-SVM for hysteretic structural system identification , 2006 .

[8]  Chih-Chen Chang,et al.  Neural Network Modeling of a Magnetorheological Damper , 1998 .

[9]  Robert D. Hanson,et al.  Electrorheological Dampers, Part II: Testing and Modeling , 1996 .

[10]  Shirley J. Dyke,et al.  PHENOMENOLOGICAL MODEL FOR MAGNETORHEOLOGICAL DAMPERS , 1997 .

[11]  Stefan Hurlebaus,et al.  Application of semi-active control strategies for seismic protection of buildings with MR dampers , 2010 .

[12]  Qing Liu,et al.  Support vector machine based semi‐active control of structures: a new control strategy , 2011 .

[13]  R. S. Jangid,et al.  SEMI-ACTIVE MR DAMPERS FOR SEISMIC CONTROL OF STRUCTURES , 2009 .

[14]  Norman M. Wereley,et al.  Dynamic characterization and analysis of magnetorheological damper behavior , 1998, Smart Structures.

[15]  Junsheng Cheng,et al.  Application of support vector regression machines to the processing of end effects of Hilbert Huang transform , 2007 .

[16]  Billie F. Spencer,et al.  Large-scale MR fluid dampers: modeling and dynamic performance considerations , 2002 .

[17]  L. Megget,et al.  Bulletin of the New Zealand society for earthquake engineering , 2002 .

[18]  Nikola Pavesic,et al.  Training RBF networks with selective backpropagation , 2004, Neurocomputing.

[19]  Hoon Sohn,et al.  Damage diagnosis under environmental and operational variations using unsupervised support vector machine , 2009 .

[20]  Mauricio Zapateiro,et al.  Neural Network Modeling of a Magnetorheological Damper , 2007, CCIA.

[21]  Hyung-Jo Jung,et al.  Vibration mitigation of highway isolated bridge using MR damper-based smart passive control system employing an electromagnetic induction part , 2009 .

[22]  Y. Wen.  Method for Random Vibration of Hysteretic Systems , 1976 .

[23]  Hassan Haji Kazemi,et al.  Semi-active Control of Structures Using Neuro-Predictive Algorithm for MR Dampers , 2008 .

[24]  J. L. Sproston,et al.  Non-linear modelling of an electro-rheological vibration damper , 1987 .

[25]  Lijuan Cao,et al.  Support vector machines experts for time series forecasting , 2003, Neurocomputing.

[26]  Guangqiang Yang.  LARGE-SCALE MAGNETORHEOLOGICAL FLUID DAMPER FOR VIBRATION MITIGATION : MODELING , TESTING AND CONTROL A Dissertation Submitted to the Graduate School of the University of Notre Dame in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy , 2001 .

[27]  B. F. Spencer,et al.  STATE OF THE ART OF STRUCTURAL CONTROL , 2003 .

[28]  Shirley J. Dyke,et al.  Semiactive Control Strategies for MR Dampers: Comparative Study , 2000 .

[29]  Jian Zhang,et al.  Support vector regression for structural identification , 2005, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[30]  Hui Li,et al.  Analysis of capability for semi‐active or passive damping systems to achieve the performance of active control systems , 2010 .

[31]  Yutaka Inoue,et al.  Overview of the application of active/semiactive control to building structures in Japan , 2001 .

[32]  B. Sp,et al.  State of the Art of Structural Control , 2003 .

[33]  Lai Ming,et al.  Nonlinear structural response prediction based on support vector machines , 2008 .

[34]  N. Wereley,et al.  Idealized Hysteresis Modeling of Electrorheological and Magnetorheological Dampers , 1998 .

[35]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[36]  Nicos Makris,et al.  RIGIDITY–PLASTICITY–VISCOSITY: CAN ELECTRORHEOLOGICAL DAMPERS PROTECT BASE‐ISOLATED STRUCTURES FROM NEAR‐SOURCE GROUND MOTIONS? , 1997 .