Predictive stability indicator: a novel approach to configuring a real‐time hybrid simulation

Summary Real-time hybrid simulation (RTHS) is an effective and versatile tool for the examination of complex structural systems with rate dependent behaviors. To meet the objectives of such a test, appropriate consideration must be given to the partitioning of the system into physical and computational portions (i.e., the configuration of the RTHS). Predictive stability and performance indicators (PSI and PPI) were initially established for use with only single degree-of-freedom systems. These indicators allow researchers to plan a RTHS, to quantitatively examine the impact of partitioning choices on stability and performance, and to assess the sensitivity of an RTHS configuration to de-synchronization at the interface. In this study, PSI is extended to any linear multi-degree-of-freedom (MDOF) system. The PSI is obtained analytically and it is independent of the transfer system and controller dynamics, providing a relatively easy and extremely useful method to examine many partitioning choices. A novel matrix method is adopted to convert a delay differential equation to a generalized eigenvalue problem using a set of vectorization mappings, and then to analytically solve the delay differential equations in a computationally efficient way. Through two illustrative examples, the PSI is demonstrated and validated. Validation of the MDOF PSI also includes comparisons to a MDOF dynamic model that includes realistic models of the hydraulic actuators and the control-structure interaction effects. Results demonstrate that the proposed PSI can be used as an effective design tool for conducting successful RTHS. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Arun Prakash,et al.  Establishing a stability switch criterion for effective implementation of real-time hybrid simulation , 2014 .

[2]  Richard Sause,et al.  Accurate real‐time hybrid earthquake simulations on large‐scale MDOF steel structure with nonlinear viscous dampers , 2015 .

[3]  James Louisell,et al.  A matrix method for determining the imaginary axis eigenvalues of a delay system , 2001, IEEE Trans. Autom. Control..

[4]  Xiuyu Gao,et al.  Real time hybrid simulation: from dynamic system, motion control to experimental error , 2013 .

[5]  James M. Ricles,et al.  Stability analysis for real‐time pseudodynamic and hybrid pseudodynamic testing with multiple sources of delay , 2008 .

[6]  D. Wagg,et al.  Real-time dynamic substructuring in a coupled oscillator–pendulum system , 2006, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[7]  Adam Mueller,et al.  A BENCHMARK TESTING SYSTEM FOR REAL-TIME HYBRID SIMULATION DEVELOPMENT , 2013 .

[8]  Shirley J. Dyke,et al.  An experimental study of MR dampers for seismic protection , 1998 .

[9]  Bin Wu,et al.  Robust integrated actuator control: experimental verification and real‐time hybrid‐simulation implementation , 2015 .

[10]  Billie F. Spencer,et al.  Feedforward actuator controller development using the backward-difference method for real-time hybrid simulation , 2014 .

[11]  Brian M. Phillips Model-based feedforward-feedback control for real-time hybrid simulation of large-scale structures , 2012 .

[12]  Feng Jin,et al.  Stability analysis of MDOF real‐time dynamic hybrid testing systems using the discrete‐time root locus technique , 2015 .

[13]  Eleni Chatzi,et al.  Experimental application of on-line parametric identification for nonlinear hysteretic systems with model uncertainty , 2010 .

[14]  James M. Ricles,et al.  Evaluation of a real-time hybrid simulation system for performance evaluation of structures with rate dependent devices subjected to seismic loading , 2012 .

[15]  Rui M. Botelho,et al.  Effective Control of a Six Degree of Freedom Shake Table , 2015 .

[16]  James M. Ricles,et al.  Large-scale real-time hybrid simulation for evaluation of advanced damping system performance , 2015 .

[17]  P. Benson Shing,et al.  Application of Pseudodynamic Test Method to Structural Research , 1996 .

[18]  Arun Prakash,et al.  Establishing a predictive performance indicator for real‐time hybrid simulation , 2014 .

[19]  Richard Christenson,et al.  Large-Scale Experimental Verification of Semiactive Control through Real-Time Hybrid Simulation , 2008 .

[20]  Shirley J. Dyke,et al.  Role of Control-Structure Interaction in Protective System Design , 1995 .

[21]  Wei Chen,et al.  Thermal and Mechanical Modeling of Load-Bearing Cold-Formed Steel Wall Systems in Fire , 2014 .

[22]  M. Nakashima,et al.  Japanese Activities on On‐Line Testing , 1987 .

[23]  Narutoshi Nakata,et al.  Substructure Shake Table Testing with Force Controlled Actuators , 2014 .

[24]  Andrei M. Reinhorn,et al.  Real-Time Hybrid Simulation Using Shake Tables and Dynamic Actuators , 2011 .

[25]  James M. Ricles,et al.  Tracking Error-Based Servohydraulic Actuator Adaptive Compensation for Real-Time Hybrid Simulation , 2010 .

[26]  Anthony Joseph Friedman Development and experimental validation of a new control strategy considering device dynamics for large-scale MR dampers using real-time hybrid simulation , 2012 .

[27]  Stephen A. Mahin,et al.  Pseudodynamic Test Method—Current Status and Future Directions , 1989 .

[28]  David J. Wagg,et al.  Stability analysis of real‐time dynamic substructuring using delay differential equation models , 2005 .

[29]  James M. Ricles,et al.  Performance Validations of Semiactive Controllers on Large-Scale Moment-Resisting Frame Equipped with 200-kN MR Damper Using Real-Time Hybrid Simulations , 2014 .

[30]  Patrick T. Brewick,et al.  Exploration of the Impacts of Driving Frequencies on Damping Estimates , 2015 .

[31]  Billie F. Spencer,et al.  Model-Based Multiactuator Control for Real-Time Hybrid Simulation , 2013 .

[32]  James M. Ricles,et al.  Experimental Studies on Real-Time Testing of Structures with Elastomeric Dampers , 2009 .