Model Order Reduction for Real-Time Hybrid Simulation: Comparing Polynomial Chaos Expansion and Neural Network methods

Tsokanas, Nikolaos; Simpson, Thomas J.; Pastorino, Roland; Chatzi, Eleni; Stojadinović, Božidar

doi:10.31224/osf.io/h2bnm

Cited by 3 publications

(3 citation statements)

References 40 publications

(46 reference statements)

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“…Not completing the computations to establish the state of the NS on time introduces delays and risks distorting the time scale of HS. In such 2 D R A F T cases, model order reduction of the NS is a very effective countermeasure [2,3].…”

Section: Introductionmentioning

confidence: 99%

Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation

Tsokanas¹,

Pastorino²,

Stojadinović³

2021

Preprint

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Real-time hybrid simulation is an experimental method used to obtain the dynamic response of a system whose components consist of loading-rate-sensitive physical and numerical substructures. The coupling of these substructures is achieved by actuation systems, i.e., an arrangement of motors or actuators, which are responsible for continuously synchronizing the interfaces of the substructures and are commanded in closed-loop control setting. To ensure high fidelity of such hybrid simulations, performing them in real-time is necessary. However, real-time hybrid simulation poses challenges as the inherent dynamics of the actuation system introduce time delays, thus modifying the dynamic response of the investigated system and hence compromising the simulation's fidelity and trust in the obtained response quantities. Therefore, a reference tracking controller is required to adequately compensate for such time delays.In this study, a novel tracking controller is proposed for dynamics compensation in real-time hybrid simulations. It is based on an adaptive model predictive control approach, a linear time-varying Kalman filter, and a real-time model identification algorithm. Within the latter, auto-regressive exogenous polynomial models are identified in real-time to estimate the changing plant dynamics and used to update the prediction model of the tracking controller. A parametric virtual real-time hybrid simulation case study is used to validate the performance and robustness of the proposed control scheme. Results demonstrate the effectiveness of the proposed controller for real-time hybrid simulations.

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Section: Introductionmentioning

confidence: 99%

Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation

Tsokanas¹,

Pastorino²,

Stojadinović³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…From the coupling of substructures, several challenges arise, e.g., time delays due to inherent transfer system dynamics or due to computational power needed to compute the NS response. Advanced control techniques [1][2][3][4] and model order reduction methods [5,6] have been used to tackle such issues. Albeit the challenges, the HS approach is beneficial since it can be used to experimentally study the inner workings of specific substructures over their linear regime and, hence, acquire realistic results but without constructing the entire considered system nor risking damaging it.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation

Tsokanas

Pastorino

Stojadinović

2021

MAKE

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Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation is extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation are conceived as being deterministic. However, associated uncertainties are present, whilst simulation deviation, due to their presence, could be significant. In this regard, global sensitivity analysis based on Sobol’ indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol’ sensitivity indices requires an unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques using machine learning data-driven regression are utilized to alleviate this burden. This study extends the current global sensitivity analysis practices in hybrid simulation by employing various different surrogate modeling methodologies as well as providing comparative results. In particular, polynomial chaos expansion, Kriging and polynomial chaos Kriging are used. A case study encompassing a virtual hybrid model is employed, and hybrid model response quantities of interest are selected. Their respective surrogates are developed, using all three aforementioned techniques. The Sobol’ indices obtained utilizing each examined surrogate are compared with each other, and the results highlight potential deviations when different surrogates are used.

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“…time delays due to inherent transfer system dynamics or due to computational power needed to compute the NS response. Advance control techniques [1,2] and model order reduction methods [3,4] have been used to tackle such issues. Albeit the challenges, the HS approach is beneficial since it can be used to experimentally study the inner workings of specific substructures over their linear regime and hence acquire realistic results but without constructing the entire considered system nor risking damaging it.…”

Section: Introductionmentioning

confidence: 99%

A comparison of surrogate modeling techniques for global sensitivity analysis in hybrid simulation

Tsokanas¹,

Pastorino²,

Stojadinović³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Hybrid simulation is a method used to investigate the dynamic response of a system subjected to a realistic loading scenario. The system under consideration is divided into multiple individual loading-rate-sensitive substructures, out of which one or more are tested physically, whereas the remaining are simulated numerically. The coupling of all substructures forms the so-called hybrid model. Although hybrid simulation has been extensively used across various engineering disciplines, it is often the case that the hybrid model and related excitation is conceived as deterministic. However, associated uncertainties are present whilst simulation deviation due to their presence could be significant. To this regard, global sensitivity analysis based on Sobol' indices can be used to determine the sensitivity of the hybrid model response due to the presence of the associated uncertainties. Nonetheless, estimation of the Sobol' sensitivity indices requires unaffordable amount of hybrid simulation evaluations. Therefore, surrogate modeling techniques are used to alleviate this burden. In this paper, three different surrogate modeling methods are examined, namely polynomial chaos expansion, Kriging and polynomial chaos Kriging. A case study encompassing a virtual hybrid model is employed and hybrid model response quantities of interest are selected. Their respective surrogates are developed using all three aforementioned techniques. The Sobol' indices obtained utilizing each examined surrogate are compared with each other and results highlight potential deviations.

show abstract

Model Order Reduction for Real-Time Hybrid Simulation: Comparing Polynomial Chaos Expansion and Neural Network methods

Cited by 3 publications

References 40 publications

Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation

Adaptive model predictive control for actuation dynamics compensation in real-time hybrid simulation

A Comparison of Surrogate Modeling Techniques for Global Sensitivity Analysis in Hybrid Simulation

A comparison of surrogate modeling techniques for global sensitivity analysis in hybrid simulation

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