Benchmark control problem for real-time hybrid simulation

Silva, Christian E.; Gómez, Daniel; Maghareh, Amin; Dyke, Shirley J.; Spencer, Billie F.

doi:10.1016/j.ymssp.2019.106381

Cited by 83 publications

(88 citation statements)

References 50 publications

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“…J 2 represents how quantitatively different the reference and measured signals are accounting for the whole simulation period, whilst J 3 accounts only for the maximum value of the tracking error. The performance of the tracking controller is assessed by how close to zero J 1 , J 2 , and J 3 are (Silva et al, 2020).…”

Section: Case Studiesmentioning

confidence: 99%

Robust Model Predictive Control for Dynamics Compensation in Real-Time Hybrid Simulation

Tsokanas

Wagg

Stojadinović

2020

Front. Built Environ.

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Hybrid simulation is an efficient method to obtain the response of an emulated system subjected to dynamic excitation by combining loading-rate-sensitive numerical and physical substructures. In such simulations, the interfaces between physical and numerical substructures are usually implemented using transfer systems, i.e., an arrangement of actuators. To guarantee high fidelity of the simulation outcome, conducting hybrid simulation in hard real-time is required. Albeit attractive, real-time hybrid simulation comes with numerous challenges, such as the inherent dynamics of the transfer system used, along with communication interrupts between numerical and physical substructures, that introduce time delays to the overall hybrid model altering the dynamic response of the system under consideration. Hence, implementation of adequate control techniques to compensate for such delays is necessary. In this study, a novel control strategy is proposed for time delay compensation of actuator dynamics in hard real-time hybrid simulation applications. The method is based on designing a transfer system controller consisting of a robust model predictive controller along with a polynomial extrapolation algorithm and a Kalman filter. This paper presents a proposed tracking controller first, followed by two virtual real-time hybrid simulation parametric case studies, which serve to validate the performance and robustness of the novel control strategy. Real-time hybrid simulation using the proposed control scheme is demonstrated to be effective for structural performance assessment.

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Section: Case Studiesmentioning

confidence: 99%

Robust Model Predictive Control for Dynamics Compensation in Real-Time Hybrid Simulation

Tsokanas

Wagg

Stojadinović

2020

Front. Built Environ.

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“…The good correspondence between the hybrid simulation result and the analytical model result validated the accuracy of the test method at the elastic stage. Dyke [24] proposed nine indexes to evaluate the accuracy of the hybrid simulation process. As shown in Equations (7) and (8), J 2 and J 3 could be applied to evaluate the test results in this paper.…”

Section: Structural Responsementioning

confidence: 99%

Hybrid Simulation of Seismic Responses of a Typical Station with a Reinforced Concrete Column

2020

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During the 1995 Kobe earthquake, damages were observed in the Daikai subway station and adjacent tunnels. It was the first large-scale underground structure that failed under the earthquake excitation. Numerical and experimental analyses have been conducted to study the failure process of the Daikai station. However, the issue of the scale ratio still exists in the shaking table tests of underground structures. In order to tackle this issue, a hybrid simulation technique is developed here to study the seismic performance of a typical subway station. Based on the previous research, it is found that the central column is the critical component of the structure. Therefore, a reinforced concrete central column is physically tested in the hybrid simulation process. On the other hand, the remaining parts of the structure and soil domain are numerically modeled at the same time. Four hybrid simulation cases are conducted with peak ground accelerations of 0.01 g, 0.1 g, 0.22 g, and 0.58 g. The test results of displacement and shear force are compared with the analytical results. Moreover, the good agreement between the test results and numerical results validate the accuracy of the proposed hybrid test method. After the hybrid simulation process, a quasi-static test is conducted to illustrate the mechanical properties of the central column after the earthquake excitation. main way to study the seismic performance of subway stations, especially for failure progress [3][4][5][6][7]. Such investigations give clues to the design of typical underground structures.Yasuo et al. [3] used the equivalent linearized constitutive model for soil modeling. The viscous boundary was selected at the bottom of the finite element (FE) model. The transmission boundary was selected laterally. The rigid beam element and elastic beam element were selected for the structure modelling. The results showed that the shear force of the center column reached the bearing capacity during the earthquake and brittle failure happened. Due to the damage of the concrete, the vertical bearing capacity of the central column decreased, and the thrust exceeded the bearing capacity, which caused the center column to fail. The upper end of the side walls and the upper roof reached the ultimate bending capacity after the center column crushed. Huo et al. [4] applied an equivalent linearized constitutive material for soil modelling. The rigid boundary was applied on the bottom side, and the free boundary was selected on the lateral sides. The results showed that the soil-structure stiffness ratio was significant in the seismic analysis. The station with a lower stiffness ratio suffered more severe damage. As the Daikai station was designed subject to static loading only, the stirrup reinforcement ratio was relatively low, and brittle failure occurred due to the insufficient shear capacity. After the shear failure, the compressive bearing capacity of the central column decreased. In fact, the Daikai station had a large span. After the center column failed, the enti...

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“…Such a benchmark problem is recently proposed by Silva et al (2020), who developed a vRTHS framework for the purpose of evaluating different control regimes on a unified system. This benchmark consists of a 2-bay, 3-story steel frame, in which the structural mass is concentrated on the floors, motion is allowed only horizontally on a single direction and damping is proportional.…”

Section: The Vrths Benchmark Structurementioning

confidence: 99%

“…We assess our AIC method via the recently established linear vRTHS benchmark problem of Silva et al (2020). It must be emphasized that, whilst the benchmark problem dealt with herein considers the control of a linear plant, the AIC framework has previously been successfully extended to nonlinear systems on multiple occasions.…”

Section: Introductionmentioning

confidence: 99%

“…The contributions of this study are (i) the treatment of RTHS in a purely data-driven fashion; (ii) the establishment of a set of specifications that such an approach should meet; (iii) the derivation of a simple, plug-n-play, adaptive modeling framework for the proper estimation of a feed-forward adaptive inverse controller for RTHS; and (iv) the assessment of our methodology via the vRTHS benchmark problem of Silva et al (2020). The context is structured as follows: in section 2 the problem is formulated, along with the list of specifications and a brief introduction to adaptive filtering is offered.…”

Section: Introductionmentioning

confidence: 99%

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Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control Plants

Simpson

Dertimanis

Chatzi

2020

Front. Built Environ.

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We present a method for control in real-time hybrid simulation (RTHS) that relies exclusively on data processing. Our approach bypasses conventional control techniques, which presume availability of a mathematical model for the description of the control plant (e.g., the transfer system and the experimental substructure) and applies a simple plug 'n play framework for tuning of an adaptive inverse controller for use in a feedforward manner, avoiding thus any feedback loops. Our methodology involves (i) a forward adaptation part, in which a noise-free estimate of the control plant's dynamics is derived; (ii) an inverse adaptation part that performs estimation of the inverse controller; and (iii) the integration of a standard polynomial extrapolation algorithm for the compensation of the delay. One particular advantage of the method is that it requires tuning of a limited set of hyper-parameters (essentially three) for proper adaptation. The efficacy of our framework is assessed via implementation on a virtual RTHS (vRTHS) benchmark problem that was recently made available to the community. The attained results indicate that data-driven RTHS may form a competitive alternative to conventional control.

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Benchmark control problem for real-time hybrid simulation

Cited by 83 publications

References 50 publications

Robust Model Predictive Control for Dynamics Compensation in Real-Time Hybrid Simulation

Robust Model Predictive Control for Dynamics Compensation in Real-Time Hybrid Simulation

Hybrid Simulation of Seismic Responses of a Typical Station with a Reinforced Concrete Column

Towards Data-Driven Real-Time Hybrid Simulation: Adaptive Modeling of Control Plants

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