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

Abstract

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

A Reflective Framework for Performance Management (REFORM) of Real-Time Hybrid Simulation

“…An essential building block in REFORM-I is the run-time thresholds and indicators to facilitate safe and accurate experiments. Stability and accuracy of a particular RTHS configuration are mainly functions of four factor: (1) overall dynamics of the reference structure; (2) fidelity of the computational substructure (related to computational workload and simulation rate); (3) partitioning configuration; and (4) capability to implement interface conditions (related to control workload and simulation rate) (Maghareh et al, 2017). In this building block, we have established run-time sensitivity indicator, run-time sensitivity envelope, and run-time stability threshold based on the first, third, and fourth factors.…”

“…In REFORM-I, we developed the run-time sensitivity indicator based on the knowledge gained in developing predictive (before any testing) stability and performance indicators intended to support the design of challenging RTHS experiments (Maghareh et al, 2014(Maghareh et al, , 2017. For these predictive indicators, the sensitivity in partitioning of any linear or piecewise linear partitioned system to interface desynchronization is computed before the testing.…”

“…The run-time sensitivity indicator (and runtime sensitivity envelope), however, are computed based on the sensitivity of the potential partitioning associated with the computational substructure and the estimated model (and associated uncertainties) of the physical substructure in realtime. To compute these in real-time, we: (1) characterize the potential critical time delay (i.e., the delay associated with the first occurrence of stability switch in Figure 6) associated with a wide and specific range for parametric variations (α 1 , α 2 ,... α n , see Figure 7) in the potential physical substructure model using the method developed in Maghareh et al (2017); (2) update the physical substructure model in real-time using a particle filter;…”

“…Further, the predictive indicators reveal that when using the same transfer system control strategy, a researcher should expect somewhat less accuracy in the FIGURE 7 | Geometric representation of mapping from an n-dimensional hyperplane associated with varying parameters of the physical substructure model to the RSI. Prior to conducting an experiment, the range of possible values of the RSI is classified, on the basis of system instability as extremely sensitive (red), moderately sensitive (yellow), and slightly sensitive (green) (Maghareh et al, 2017;Silva et al, 2020). results associated with Case 4 compared to the results from Cases 1-3.…”

Online Stability Analysis for Real-Time Hybrid Simulation Testing

“…Mercan and Ricles (2008) considered pseudo-delay techniques for determining the size of delay to initiate instability. Maghareh et al (2017) established a predictive stability indicator, demonstrating that some partitioning choices are more sensitive to delay than others. Gao and You (2019) proposed generalized EOM for the hybrid system to predict the RTHS stability limit.…”