Seismic Risk–Based Stochastic Optimal Control of Structures Using Magnetorheological Dampers

AbstractMagnetorheological (MR) dampers are regarded as among the most promising control devices owing to their perfect dynamic damping behaviors. The operating efficiency of MR dampers, however, upon randomly excited structural systems remains a challenge because the conventional schemes employing linear quadratic Gaussian (LQG) control lack a logical treatment of randomness inherent in external excitations. A scheme of physically based stochastic optimal control designed to bypass the dilemma was proposed in recent years. To this end, in the present paper, a design and optimization procedure for the semi-active control of randomly base-excited structures with MR dampers is developed. Stochastic modeling of seismic ground motions as a result of the source properties and propagation path is carried out. The control efficiency of MR damped structures with respect to seismic risk and variation is investigated. Numerical results reveal that MR damping control can strengthen the seismic safety of structures s...

[1]  Billie F. Spencer,et al.  Modeling and Control of Magnetorheological Dampers for Seismic Response Reduction , 1996 .

[2]  Billie F. Spencer,et al.  “Smart” Base Isolation Strategies Employing Magnetorheological Dampers , 2002 .

[3]  H. Zhang,et al.  Parameter Analysis of the Differential Model of Hysteresis , 2004 .

[4]  James M. Ricles,et al.  Modeling of a large‐scale magneto‐rheological damper for seismic hazard mitigation. Part II: Semi‐active mode , 2013 .

[5]  Jann N. Yang,et al.  Application of Optimal Control Theory to Civil Engineering Structures , 1975 .

[6]  T. T. Soong,et al.  STRUCTURAL CONTROL: PAST, PRESENT, AND FUTURE , 1997 .

[7]  Yumei Wang,et al.  Modal‐based LQG for smart base isolation system design in seismic response control , 2013 .

[8]  Zhou Qiang.  Two mechanic models for magnetorheological damper and corresponding test verification , 2002 .

[9]  Zhao-Dong Xu,et al.  Neuro-fuzzy control strategy for earthquake-excited nonlinear magnetorheological structures , 2008 .

[10]  Chih-Chen Chang,et al.  Shear-Mode Rotary Magnetorheological Damper for Small-Scale Structural Control Experiments , 2004 .

[11]  B. F. Spencer,et al.  Active Structural Control: Theory and Practice , 1992 .

[12]  Saiful Amri Mazlan,et al.  A design and modelling review of rotary magnetorheological damper , 2013 .

[13]  Yan Yong,et al.  Earthquake Ground Motion Modeling. II: Stochastic Line Source , 1991 .

[14]  Juan E. Carrion,et al.  Real-time hybrid simulation for structural control performance assessment , 2009 .

[15]  Sung-Kyung Lee,et al.  Performance evaluation of an MR damper in building structures considering soil–structure interaction effects , 2009 .

[16]  James M. Ricles,et al.  Comparative Studies of Semiactive Control Strategies for MR Dampers: Pure Simulation and Real-Time Hybrid Tests , 2013 .

[17]  Jan Becker Høgsberg,et al.  The role of negative stiffness in semi‐active control of magneto‐rheological dampers , 2011 .

[18]  Francesco Marazzi,et al.  Technology of Semiactive Devices and Applications in Vibration Mitigation , 2006 .

[19]  Ding Wang,et al.  Physical random function model of ground motions for engineering purposes , 2011 .

[20]  Jianbing Chen,et al.  Strategy for selecting representative points via tangent spheres in the probability density evolution method , 2008 .

[21]  Yi-Qing Ni,et al.  Neuro-control of cable vibration using semi-active magneto-rheological dampers , 2002 .

[22]  M. Rabins,et al.  Semi‐Active versus Passive or Active Tuned Mass Dampers for Structural Control , 1983 .

[23]  Jianbing Chen,et al.  Stochastic Dynamics of Structures , 2009 .

[24]  Armen Der Kiureghian,et al.  A stochastic ground motion model with separable temporal and spectral nonstationarities , 2008 .

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

[26]  Yongbo Peng,et al.  Nonlinear stochastic optimal control strategy of hysteretic structures , 2011 .

[27]  Masayuki Kohiyama,et al.  Vibration characteristics change of a base-isolated building with semi-active dampers before, during, and after the 2011 Great East Japan earthquake , 2015 .

[28]  Adrian M. Chandler,et al.  Simplified inverse dynamics models for MR fluid dampers , 2006 .

[29]  Erik A. Johnson,et al.  Smart base‐isolated benchmark building part IV: Phase II sample controllers for nonlinear isolation systems , 2006 .

[30]  Zhao-Dong Xu,et al.  Performance tests and mathematical model considering magnetic saturation for magnetorheological damper , 2012 .

[31]  David J. Dowrick.  Earthquake Resistant Design and Risk Reduction , 2009 .

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

[33]  María Jesús López Boada,et al.  Modeling of a magnetorheological damper by recursive lazy learning , 2011 .

[34]  George W. Housner,et al.  Characteristics of strong-motion earthquakes , 1947 .

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

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

[37]  Yongbo Peng,et al.  A physical approach to structural stochastic optimal controls , 2010 .

[38]  Hyung-Jo Jung,et al.  Feasibility Test of Adaptive Passive Control System Using MR Fluid Damper with Electromagnetic Induction Part , 2010 .

[39]  J. Carlson,et al.  MR fluid, foam and elastomer devices , 2000 .

[40]  Qiang Zhou,et al.  Design and Fabrication of 500-kN Large-scale MR Damper , 2011 .

[41]  Sinan Korkmaz,et al.  Review: A review of active structural control: challenges for engineering informatics , 2011 .

[42]  Yi-Qing Ni,et al.  A semi-active stochastic optimal control strategy for nonlinear structural systems with MR dampers , 2009 .

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

[44]  Chia-Ming Chang,et al.  Real-Time Hybrid Simulation of a Smart Base-Isolated Building , 2015 .