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Journal articles on the topic 'Real-time hybrid simulation'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

INABE, HIROTO. "Real-Time Digital Simulation for Power System. 4. Hybrid Real Time Simulator." Journal of the Institute of Electrical Engineers of Japan 122, no. 5 (2002): 304–6. http://dx.doi.org/10.1541/ieejjournal.122.304.

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2

Kciuk, Sławomir, Paweł Kielan, Arkadiusz Mężyk, and Krzysztof Wilk. "Hybrid Simulation of Tracked Vehicle Suspension on Real-Time Environment." Solid State Phenomena 248 (March 2016): 161–68. http://dx.doi.org/10.4028/www.scientific.net/ssp.248.161.

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The work presents simulation method of dynamic properties used as assistance in the construction process of suspension systems for high-speed tracked vehicles. Special consideration has been given to the real-time coupling of virtual models with the dynamic response of actual elastic-damping elements of the vehicles. An original design method has been proposed. The method is characterized by the fact that each of the design stages are not performed sequentially, but are parallel to each other and that at each level, mutual coupling between the tasks of the design process occurs. The proposed simulation method using the dSpace system is based on the integration of virtual environment such as LMS Virtual Lab or MATLAB/Simulink with the actual object such as a damper, by means of dedicated input/output devices operating in real time. The method developed in the work allowed for an extension of the classic co-simulations, that is, simulations in two coupled virtual environments, to include an actual component or, rather – its dynamic – often non-linear – characteristic, its response to excitation. The method developed in the work allowed for an extension of the classic co-simulations, that is, simulations in two coupled virtual environments, to include an actual component or, rather – its dynamic – often non-linear – characteristic, its response to excitation.The developed test method and the computer programs have been verified by means of experimental measurements of the dynamic characteristics of the actual object during test-ground tests and in the laboratory. The obtained results of the simulations and experiments allow to confirm the validity of the assumed thesis, which has been included in the summary.

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3

Silva, Christian E., Daniel Gomez, Amin Maghareh, Shirley J. Dyke, and Billie F. Spencer. "Benchmark control problem for real-time hybrid simulation." Mechanical Systems and Signal Processing 135 (January 2020): 106381. http://dx.doi.org/10.1016/j.ymssp.2019.106381.

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4

Leng, Feng, Chengxiong Mao, Dan Wang, Ranran An, Yuan Zhang, Yanjun Zhao, Linglong Cai, and Jie Tian. "Applications of Digital-Physical Hybrid Real-Time Simulation Platform in Power Systems." Energies 11, no. 10 (October 9, 2018): 2682. http://dx.doi.org/10.3390/en11102682.

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Digital-physical hybrid real-time simulation (hybrid simulation) platform integrates the advantages of both digital simulation and physical simulation by combining the physical simulation laboratory and the real-time digital simulator. Based on a 400 V/50 kVA hybrid simulation platform with 500 kVA short-circuit capacity, the hybrid simulation methodology and a Hausdorff distance based accuracy evaluation method are proposed. The case validation of power system fault recurrence is performed through this platform, and the stability and accuracy are further validated by comparing the hybrid simulation waveform and field-recorded waveform and by evaluating the accuracy with the proposed error index. Two typical application scenarios in power systems are studied subsequently. The static var generator testing shows the hybrid simulation platform can provide system-level testing conditions for power electronics equipment conveniently. The low-voltage ride through standard testing of a photovoltaic inverter indicates that the hybrid simulation platform can be also used for voltage standard testing for various power system apparatus with low cost. With this hybrid simulation platform, the power system simulation and equipment testing can be implemented with many advantages, such as short period of modelling, flexible modification of parameter and network, low cost, and low risk. Based on this powerful tool platform, there will be more application scenarios in future power systems.

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5

Matei, Ion, Alexander Feldman, Johan De Kleer, and Alexandre Perez. "Real time model-based diagnosis enabled by hybrid modeling." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 10. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1278.

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In this paper we propose a hybrid modeling approach for generating reduced models of a high fidelity model of a physical system. We propose machine learning inspired representations for complex model components. These representations preserve in part the physical interpretation of the original components. Training platforms featuring automatic differentiation are used to learn the parameters of the new representations using data generated by the high-fidelity model. We showcase our approach in the context of fault diagnosis for a rail switch system. We generate three new model abstractions whose complexities are two order of magnitude smaller than the complexity of the high fidelity model, both in the number of equations and simulation time. Faster simulations ensure faster diagnosis solutions and enable the use of diagnosis algorithms relying heavily on large numbers of model simulations.

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6

Dufour, Christian, Simon Abourida, and Jean Bélanger. "Real-Time Simulation of Hybrid Electric Vehicle Traction Devices." ATZautotechnology 4, no. 1 (January 2004): 44–47. http://dx.doi.org/10.1007/bf03246805.

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7

Christenson, Richard E., and Michael J. Harris. "Real-time hybrid simulation using analogue electronic computer technology." International Journal of Lifecycle Performance Engineering 4, no. 1/2/3 (2020): 25. http://dx.doi.org/10.1504/ijlcpe.2020.10031048.

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8

Harris, Michael J., and Richard E. Christenson. "Real-time hybrid simulation using analogue electronic computer technology." International Journal of Lifecycle Performance Engineering 4, no. 1/2/3 (2020): 25. http://dx.doi.org/10.1504/ijlcpe.2020.108941.

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9

Zhang, Ruiyang, and Brian M. Phillips. "Artificial Specimen Damping for Substructure Real-Time Hybrid Simulation." Journal of Engineering Mechanics 143, no. 8 (August 2017): 04017052. http://dx.doi.org/10.1061/(asce)em.1943-7889.0001242.

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10

Zhu, Bo, and Lixu Gu. "A hybrid deformable model for real-time surgical simulation." Computerized Medical Imaging and Graphics 36, no. 5 (July 2012): 356–65. http://dx.doi.org/10.1016/j.compmedimag.2012.03.001.

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11

Carrion, Juan E., B. F. Spencer, and Brian M. Phillips. "Real-time hybrid simulation for structural control performance assessment." Earthquake Engineering and Engineering Vibration 8, no. 4 (December 2009): 481–92. http://dx.doi.org/10.1007/s11803-009-9122-4.

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12

Kim, Sung Jig, Richard E. Christenson, Steven F. Wojtkiewicz, and Erik A. Johnson. "Real-time hybrid simulation using the convolution integral method." Smart Materials and Structures 20, no. 2 (January 26, 2011): 025024. http://dx.doi.org/10.1088/0964-1726/20/2/025024.

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13

Korn, Leonie, Daniel Rüschen, Niklas Zander, Steffen Leonhardt, and Marian Walter. "Real-Time ECG Simulation for Hybrid Mock Circulatory Loops." Artificial Organs 42, no. 2 (October 12, 2017): 131–40. http://dx.doi.org/10.1111/aor.13000.

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14

Phillips, Brian M., and Billie F. Spencer. "Model-Based Multiactuator Control for Real-Time Hybrid Simulation." Journal of Engineering Mechanics 139, no. 2 (February 2013): 219–28. http://dx.doi.org/10.1061/(asce)em.1943-7889.0000493.

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15

Zhang, Ruiyang, Brian M. Phillips, Pedro L. Fernández-Cabán, and Forrest J. Masters. "Cyber-physical structural optimization using real-time hybrid simulation." Engineering Structures 195 (September 2019): 113–24. http://dx.doi.org/10.1016/j.engstruct.2019.05.042.

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16

Waldbjoern, Jacob P., Amin Maghareh, Ge Ou, Shirley J. Dyke, and Henrik Stang. "Multi-rate Real Time Hybrid Simulation operated on a flexible LabVIEW real-time platform." Engineering Structures 239 (July 2021): 112308. http://dx.doi.org/10.1016/j.engstruct.2021.112308.

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17

Moreno-Guerra, Mario R., Oscar Martínez-Romero, Luis Manuel Palacios-Pineda, Daniel Olvera-Trejo, José A. Diaz-Elizondo, Eduardo Flores-Villalba, Jorge V. L. da Silva, Alex Elías-Zúñiga, and Ciro A. Rodriguez. "Soft Tissue Hybrid Model for Real-Time Simulations." Polymers 14, no. 7 (March 30, 2022): 1407. http://dx.doi.org/10.3390/polym14071407.

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In this article, a recent formulation for real-time simulation is developed combining the strain energy density of the Spring Mass Model (SMM) with the equivalent representation of the Strain Energy Density Function (SEDF). The resulting Equivalent Energy Spring Model (EESM) is expected to provide information in real-time about the mechanical response of soft tissue when subjected to uniaxial deformations. The proposed model represents a variation of the SMM and can be used to predict the mechanical behavior of biological tissues not only during loading but also during unloading deformation states. To assess the accuracy achieved by the EESM, experimental data was collected from liver porcine samples via uniaxial loading and unloading tensile tests. Validation of the model through numerical predictions achieved a refresh rate of 31 fps (31.49 ms of computation time for each frame), achieving a coefficient of determination R2 from 93.23% to 99.94% when compared to experimental data. The proposed hybrid formulation to characterize soft tissue mechanical behavior is fast enough for real-time simulation and captures the soft material nonlinear virgin and stress-softened effects with high accuracy.

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18

Liu, Xu Dong, Qing Wu Fan, Bang Gui Zheng, and Jian Min Duan. "Real-Time Simulation Study for a Series Hybrid Electric Vehicle." Applied Mechanics and Materials 128-129 (October 2011): 965–69. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.965.

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To shorten design period and reduce development costs, computer modeling & simulation is important for HEV design and development. In this paper, real-time simulation for a Series Hybrid Electric Vehicle (SHEV) is made to test its fuzzy logic control strategy based on dSPACE-DS1103 development kits. The whole real-time simulation schematic is designed and the vehicle forward-facing simulation model is set up. Driver behavior is simulated by two potentiometers and introduced into the system to realize close-loop control. A real-time monitoring interface is also developed to observe the experiment results. Experiment results show that the real-time simulation platform works well and the SHEV fuzzy logic control strategy is effective.

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19

Yin, X., and H. Schwarz. "A Hybrid Method for Real-Time Simulation of Continuous-Time Bilinear Systems." IFAC Proceedings Volumes 25, no. 20 (September 1992): 289–94. http://dx.doi.org/10.1016/s1474-6670(17)49877-6.

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20

Sottile, Francesco, Mauricio A. Caceres, and Maurizio A. Spirito. "A Simulation Tool for Real-Time Hybrid-Cooperative Positioning Algorithms." International Journal of Embedded and Real-Time Communication Systems 3, no. 3 (July 2012): 67–87. http://dx.doi.org/10.4018/jertcs.2012070105.

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The authors propose a simulation tool (ST) able to test real-time hybrid GNSS/terrestrial and cooperative positioning algorithms that fuse both pseudorange measurements from satellites and terrestrial range measurements based on radio frequency communication performed between nodes of a wireless network. In particular, the ST simulates devices belonging to a peer-to-peer (P2P) wireless network where peers, equipped also with a GNSS receiver, cooperate among them by exchanging aiding data in order to improve both positioning accuracy and availability. Furthermore, the authors propose a method to increase the robustness of cooperative algorithms based on the estimated position covariance matrix. In particular, the proposed approach assures a faster estimation convergence and improved accuracy while lowering computational complexity and network traffic. Finally, the authors tested the sensitivity of the implemented positioning algorithms through the ST in two different scenarios, first in presence of high level of pseudorange noise and then in presence of a malicious peer in the P2P network.

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21

Shao, Xiaoyun, Andrei M. Reinhorn, and Mettupalayam V. Sivaselvan. "Real-Time Hybrid Simulation Using Shake Tables and Dynamic Actuators." Journal of Structural Engineering 137, no. 7 (July 2011): 748–60. http://dx.doi.org/10.1061/(asce)st.1943-541x.0000314.

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22

Saouma, Victor, Gary Haussmann, Dae-Hung Kang, and Wassim Ghannoum. "Real-Time Hybrid Simulation of a Nonductile Reinforced Concrete Frame." Journal of Structural Engineering 140, no. 2 (February 2014): 04013059. http://dx.doi.org/10.1061/(asce)st.1943-541x.0000813.

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23

Ning, Xizhan, Zhen Wang, Huimeng Zhou, Bin Wu, Yong Ding, and Bin Xu. "Robust actuator dynamics compensation method for real-time hybrid simulation." Mechanical Systems and Signal Processing 131 (September 2019): 49–70. http://dx.doi.org/10.1016/j.ymssp.2019.05.038.

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24

Najafi, Amirali, and Billie F. Spencer. "Adaptive model reference control method for real-time hybrid simulation." Mechanical Systems and Signal Processing 132 (October 2019): 183–93. http://dx.doi.org/10.1016/j.ymssp.2019.06.023.

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25

Asai, Takehiko, Chia-Ming Chang, and B. F. Spencer. "Real-Time Hybrid Simulation of a Smart Base-Isolated Building." Journal of Engineering Mechanics 141, no. 3 (March 2015): 04014128. http://dx.doi.org/10.1061/(asce)em.1943-7889.0000844.

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26

Basile, Francesco, Pasquale Chiacchio, and Domenico Teta. "A hybrid model for real time simulation of urban traffic." Control Engineering Practice 20, no. 2 (February 2012): 123–37. http://dx.doi.org/10.1016/j.conengprac.2011.10.002.

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27

Li, Xin, Ali I. Ozdagli, Shirley J. Dyke, Xilin Lu, and Richard Christenson. "Development and Verification of Distributed Real-Time Hybrid Simulation Methods." Journal of Computing in Civil Engineering 31, no. 4 (July 2017): 04017014. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000654.

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28

Chen, Cheng, James M. Ricles, and Tong Guo. "Improved Adaptive Inverse Compensation Technique for Real-Time Hybrid Simulation." Journal of Engineering Mechanics 138, no. 12 (December 2012): 1432–46. http://dx.doi.org/10.1061/(asce)em.1943-7889.0000450.

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29

Chen, Pei-Ching, Chia-Ming Chang, Billie F. Spencer, and Keh-Chyuan Tsai. "Adaptive model-based tracking control for real-time hybrid simulation." Bulletin of Earthquake Engineering 13, no. 6 (September 27, 2014): 1633–53. http://dx.doi.org/10.1007/s10518-014-9681-2.

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30

Zhu, Fei, Jin-Ting Wang, Feng Jin, and Yao Gui. "Comparison of explicit integration algorithms for real-time hybrid simulation." Bulletin of Earthquake Engineering 14, no. 1 (September 21, 2015): 89–114. http://dx.doi.org/10.1007/s10518-015-9816-0.

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31

Su, Weihua, and Wei Song. "A real-time hybrid aeroelastic simulation platform for flexible wings." Aerospace Science and Technology 95 (December 2019): 105513. http://dx.doi.org/10.1016/j.ast.2019.105513.

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32

Maghareh, Amin, Shirley J. Dyke, Arun Prakash, and Gregory B. Bunting. "Establishing a predictive performance indicator for real-time hybrid simulation." Earthquake Engineering & Structural Dynamics 43, no. 15 (June 26, 2014): 2299–318. http://dx.doi.org/10.1002/eqe.2448.

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33

Shi, Lei, and Xiang Ning Xiao. "Power System Subsynchronous Oscillations Electromagnetic and Electromechanical Transient Hybrid Real-Time Simulation Based on RTDS." Advanced Materials Research 433-440 (January 2012): 2850–55. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2850.

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In this paper, power system electromagnetic and electromechanical transient hybrid real-time simulation technology is expounded in details, which has advantages of the electromagnetic transient simulation program and electromechanical ones. The more details needed to analyze the dynamic characteristics of power systems are provided by this hybrid simulation technology, and the scale of power system simulated is not limited in the hybrid simulation program. The hybrid simulation program is applied to analyze the power system subsynchronous oscillation problem occurred in a power plant with 4 turbine generators located in the northwestern China. According to the simulation results, it is clear that the stability of generators is threatened by the subsynchronous oscillations caused by capacitor series compensation in the transmission line connecting the power plant and the load center system. In meantime, the effectiveness of the countermeasure is validated simultaneously by the hybrid simulation results.

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34

Pan, Sisi, Wei Jiang, Ming Li, Hua Geng, and Jieyun Wang. "Evaluation of the Communication Delay in a Hybrid Real-Time Simulator for Weak Grids." Energies 15, no. 6 (March 19, 2022): 2255. http://dx.doi.org/10.3390/en15062255.

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Real-time Simulation (RTS) is one of the effective means via which to study device level or system level dynamics, such as power converter online testing, evaluation, and control, and power system stability analysis. The RTS -enabled design-chain offers a time -effective, low-cost, and fail-safe development process. As the penetration of renewable energy is becoming higher, the demand in hybrid system real-time simulation becomes imperative, where fast-dynamic device level power converters and slow -dynamic large -scale power systems are simulated at the same time. This paper introduces a novel hybrid real-time simulation architecture based on the central processing unit (CPU) and the field-programmable gate array (FPGA). Compared with the off-the-shelf power system real-time simulation system, it offers both wide time scale simulation and high accuracy. The multi-time scale model can perform electromechanical electromagnetic transient hybrid simulation, which can be applied to the research of power systems penetrated with power converters. In the proposed simulation platform, the communication delay is introduced when different RTS platforms exchange real-time data. The communication delay should be considered in the stability analysis of the grid-connected inverters in a weak grid environment. Based on the virtual impedance characteristic formed by the control loop with and without communication delay, the impedance characteristics are analyzed and inter-simulator delay impacts are revealed in this paper. Theoretical analysis indicates that the communication delay, contrary to expectation, can improve the virtual impedance characteristics of the system. With the same hardware simulation parameters, the grid-converter system is verified on both the Typhoon system alone and the Typhoon-dSPACE-SpaceR hybrid simulation platform. The THD value of grid current in a weak grid environment that works in the Typhoon system is 4.98%, and 2.38% in the Typhoon-dSPACE-SpaceR hybrid simulation platform. This study eventually reveals the fact that the inter-simulation delay creates the illusion that the control system built in the novel hybrid real-time simulation is more stable under weak grid conditions.

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35

Ning, Xizhan. "Mixed Sensitivity-Based Robust H∞ Control Method for Real-Time Hybrid Simulation." Symmetry 13, no. 5 (May 10, 2021): 840. http://dx.doi.org/10.3390/sym13050840.

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Real-time hybrid simulation (RTHS), dividing the emulated structure into numerical substructures (NS) and physical substructures (PS), is a powerful technique to obtain responses and then to assess the seismic performance of civil engineering structures. A transfer system, a servo-hydraulic actuator or shaking table, is used to apply boundary conditions between the two substructures. However, the servo-hydraulic actuator is inherently a complex system with nonlinearities and may introduce time delays into the RTHS, which will decrease the accuracy and stability of the RTHS. Moreover, there are various uncertainties in RTHS. An accurate and robust actuator control strategy is necessary to guarantee reliable simulation results. Therefore, a mixed sensitivity-based H∞ control method was proposed for RTHS. In H∞ control, the dynamics and robustness of the closed-loop transfer system are realized by performance weighting functions. A form of weighting function was given considering the requirement in RTHS. The influence of the weighting functions on the dynamics was investigated. Numerical simulations and actual RTHSs were carried out under symmetric and asymmetric dynamic loads, namely sinusoidal and earthquake excitation, respectively. Results indicated that the H∞ control method used for RTHS is feasible, and it exhibits an excellent tracking performance and robustness.

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36

Eem, Seung-Hyun, Jeong-Hoi Koo, and Hyung-Jo Jung. "Feasibility study of an adaptive mount system based on magnetorheological elastomer using real-time hybrid simulation." Journal of Intelligent Material Systems and Structures 30, no. 5 (January 29, 2018): 701–7. http://dx.doi.org/10.1177/1045389x18754347.

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This article investigates an adaptive mount system based on magnetorheological elastomer in reducing the vibration of an equipment on the isolation table. Incorporating MR elastomers, whose elastic modulus or stiffness can be adjusted depending on the applied magnetic field, the proposed mount system strives to alleviate the limitations of existing passive-type mount systems. The primary goal of this study is to evaluate the vibration reduction performance of the proposed MR elastomer mount using the hybrid simulation technique. For real-time hybrid simulations, the MR elastomer mount and the control system are used as an experimental part, which is installed on the shaking table, and an equipment on the table is used as a numerical part. A suitable control algorithm is designed for the real-time hybrid simulations to avoid the responses of the equipment’s natural frequency by tracking the frequencies of the responses. After performing a series of real-time hybrid simulation on the adaptive mount system and the passive-type mount system under sinusoidal excitations, this study compares the effectiveness of the adaptive mount system over its passive counterpart. The results show that the proposed adaptive elastomer mount system outperforms the passive-type mount system in reducing the responses of the equipment for the excitations considered in this study.

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37

Yang, Chen, Kangjie Deng, Hangxing He, Haochuang Wu, Kai Yao, and Yuanzhe Fan. "Real-Time Interface Model Investigation for MCFC-MGT HILS Hybrid Power System." Energies 12, no. 11 (June 8, 2019): 2192. http://dx.doi.org/10.3390/en12112192.

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The research on the control strategy and dynamic characteristics of the Molten Carbonate Fuel Cell-Micro Gas Turbine (MCFC-MGT) hybrid power system has received much attention. The use of the Hardware-In-the-Loop Simulation (HILS) method to study the MCFC-MGT hybrid power system, where the MCFC is the model subsystem and the MGT is the physical subsystem, is an effective means to save development cost and time. The difficulty with developing the MCFC-MGT HILS system is the transfer of the mass, energy, and momentum between the physical subsystem and the model subsystem. Hence, a new Simulation–Stimulation (Sim–Stim) interface model of the MCFC-MGT HILS hybrid power system to achieve a consistent mass, energy, and momentum with the prototype system of the MCFC-MGT hybrid power system is proposed. In order to validate the Sim–Stim interface model before application in an actual system, both a real-time model of the MCFC-MGT hybrid power system and the MCFC-MGT HILS hybrid power system based on the Sim–Stim interface model were developed in the Advanced PROcess Simulation (APROS) platform. The step-up and step-down of the current density, which were strict for the Sim–Stim interface model, were studied in these two models. The results demonstrated that the Sim–Stim interface model developed for the MCFC-MGT HILS hybrid power system is rapid and reasonable.

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38

Li, Yang, Guoqing He, and Xing Zhang. "Research on AC/DC hybrid system real-time simulation progress improvement." IOP Conference Series: Earth and Environmental Science 675, no. 1 (February 1, 2021): 012081. http://dx.doi.org/10.1088/1755-1315/675/1/012081.

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39

Sun, Chao, Wei Song, and Vahid Jahangiri. "A real-time hybrid simulation framework for floating offshore wind turbines." Ocean Engineering 265 (December 2022): 112529. http://dx.doi.org/10.1016/j.oceaneng.2022.112529.

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40

Li, Hong-Wei, Fang Wang, Yi-Qing Ni, You-Wu Wang, and Zhao-Dong Xu. "An Adaptive and Robust Control Strategy for Real-Time Hybrid Simulation." Sensors 22, no. 17 (August 31, 2022): 6569. http://dx.doi.org/10.3390/s22176569.

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A real-time hybrid simulation (RTHS) is a promising technique to investigate a complicated or large-scale structure by dividing it into numerical and physical substructures and conducting cyber-physical tests on it. The control system design of an RTHS is a challenging topic due to the additional feedback between the physical and numerical substructures, and the complexity of the physical control plant. This paper proposes a novel RTHS control strategy by combining the theories of adaptive control and robust control, where a reformed plant which is highly simplified compared to the physical plant can be used to design the control system without compromising the control performance. The adaptation and robustness features of the control system are realized by the bounded-gain forgetting least-squares estimator and the sliding mode controller, respectively. The control strategy is validated by investigating an RTHS benchmark problem of a nonlinear three-story steel frame The proposed control strategy could simplify the control system design and does not require a precise physical plant; thus, it is an efficient and practical option for an RTHS.

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41

Najafi, Amirali, and Billie F. Spencer. "Multiaxial Real-Time Hybrid Simulation for Substructuring with Multiple Boundary Points." Journal of Structural Engineering 147, no. 11 (November 2021): 05021007. http://dx.doi.org/10.1061/(asce)st.1943-541x.0003138.

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42

Bergero, Federico, and Ernesto Kofman. "PowerDEVS: a tool for hybrid system modeling and real-time simulation." SIMULATION 87, no. 1-2 (April 28, 2010): 113–32. http://dx.doi.org/10.1177/0037549710368029.

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43

Lu, Li-Qiao, Jin-Ting Wang, and Fei Zhu. "Improvement of Real-Time Hybrid Simulation Using Parallel Finite-Element Program." Journal of Earthquake Engineering 24, no. 10 (May 2, 2018): 1547–65. http://dx.doi.org/10.1080/13632469.2018.1469442.

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44

Phillips, Brian M., and Billie F. Spencer. "Model-Based Feedforward-Feedback Actuator Control for Real-Time Hybrid Simulation." Journal of Structural Engineering 139, no. 7 (July 2013): 1205–14. http://dx.doi.org/10.1061/(asce)st.1943-541x.0000606.

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45

Dyke, Shirley J., Daniel Gomez, and Billie F. Spencer. "Editorial: Special issue on the real-time hybrid simulation benchmark problem." Mechanical Systems and Signal Processing 142 (August 2020): 106804. http://dx.doi.org/10.1016/j.ymssp.2020.106804.

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46

Shao, Xiaoyun, Adam Mueller, and Bilal Ahmed Mohammed. "Real-Time Hybrid Simulation with Online Model Updating: Methodology and Implementation." Journal of Engineering Mechanics 142, no. 2 (February 2016): 04015074. http://dx.doi.org/10.1061/(asce)em.1943-7889.0000987.

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47

Li, Tengfei, Lei Ma, Yan Sui, Mingzhou Su, and Yi Qiang. "Soft real-time hybrid simulation based on a space steel frame." Bulletin of Earthquake Engineering 18, no. 6 (February 8, 2020): 2699–722. http://dx.doi.org/10.1007/s10518-020-00798-z.

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48

Fermandois, Gaston A., and Billie F. Spencer. "Model-based framework for multi-axial real-time hybrid simulation testing." Earthquake Engineering and Engineering Vibration 16, no. 4 (October 2017): 671–91. http://dx.doi.org/10.1007/s11803-017-0407-8.

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Zhang, Liguo, Yong Ma, and Liang Shi. "A hybrid traffic flow model for real time freeway traffic simulation." KSCE Journal of Civil Engineering 18, no. 4 (April 25, 2014): 1160–64. http://dx.doi.org/10.1007/s12205-014-0635-7.

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Wang, Xuguang, Robin E. Kim, Oh-Sung Kwon, In-Hwan Yeo, and Jae-Kwon Ahn. "Continuous Real-Time Hybrid Simulation Method for Structures Subject to Fire." Journal of Structural Engineering 145, no. 12 (December 2019): 04019152. http://dx.doi.org/10.1061/(asce)st.1943-541x.0002436.

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