Adaptive Feedforward and Feedback Compensation Method for Real-time Hybrid Simulation Based on a Discrete Physical Testing System Model

Ning, Xizhan; Wang, Zhen; Wang, Chunpeng; Wu, Bin

doi:10.1080/13632469.2020.1823912

Cited by 20 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since, in the context of metamodelling, the problem is one of regression, i.e., predicting a continuous output variable, the loss function of the network to be minimized was selected as the mean squared error value, as demonstrated in Eq. (9). In this equation, the loss value J is calculated as the mean value of the squared error between the true output value Y i for the training point i and the predicted output value from the networkŶ i for all N training data points.…”

Section: Network Trainingmentioning

confidence: 99%

“…Several control strategies for RTHS have been proposed and are available in the literature. Some of the recently developed novel approaches can be found in [6,7,8,9]. Nonetheless, challenges in RTHS do not arise only from the transfer system of the PS but also from the NS side, such as from the computational power needed to compute the NS responses.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Tsokanas¹,

Simpson²,

Pastorino³

et al. 2021

Preprint

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 by combining numerical and physical substructures. To ensure high fidelity of the simulation results, it is often necessary to conduct hybrid simulation in real-time. One of the challenges arising in real-time hybrid simulation originates from high-dimensional nonlinear numerical substructures and, in particular, from the computational cost linked to the computation of their dynamic responses with sufficient accuracy. It is often the case that the simulation time-step must be decreased to capture the dynamic behavior of numerical substructures, thus resulting in longer computation. When such computation takes longer than the actual simulation time, time delays are introduced and the simulation timescale becomes distorted. In such a case, the only viable solution for doing hybrid simulation in real-time is to reduce the order of such complex numerical substructures.In this study, a model order reduction framework is proposed for real-time hybrid simulation, based on polynomial chaos expansion and feedforward neural networks. A parametric case study encompassing a virtual hybrid model is used to validate the framework. Selected numerical substructures are substituted with their respective reduced-order models. To determine the robustness of the framework, parameter sets are defined to cover the design space of interest. A comparison between the full- and reduced-order hybrid model response is delivered. The attained results demonstrate the performance of the proposed framework.

show abstract

Section: Network Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tsokanas¹,

Simpson²,

Pastorino³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…It can be seen that Equation (4a) is a linear difference equation and Ning et al [31] and Wang et al [13] employed the least-square method to estimate the system model parameter. However, the convergence of the least-square method cannot be guaranteed, and it is sometimes time consuming.…”

Section: Parameter Estimationmentioning

confidence: 99%

“…Then, adaptive law was employed to combine with inverse control [27][28][29] and model-based control [30], where the model parameters of the control plant are estimated online. Recently, a general adaptive compensation method based on a discrete model of the control plant was proposed and validated [13,31], where the compensation commands of actuators were generated using the desired displacements, measured displacements, and previous displacement commands, and the least squares method was used to estimate the system model parameters. Due to the effect of measurement noise and initial parameter values, the estimated parameters for the discrete system model changed significantly in Wang et al's method [13], especially at the beginning of the simulation.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filter-Based Adaptive Delay Compensation for Benchmark Problem in Real-Time Hybrid Simulation

Ning

Wang

2020

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Real-time hybrid simulation (RTHS) is a versatile, effective, and promising experimental method used to evaluate the structural performance under dynamic loads. In RTHS, the emulated structure is divided into a numerically simulated substructure (NS) and a physically tested substructure (PS), and a transfer system is used to ensure the force equilibrium and deformation compatibility between the substructures. Owing to the inherent dynamics of the PS and transfer system (referred to as a control plant in this study), there is a time-delay between the displacement command and measurement. This causes de-synchronization between the boundary of the PS and NS, and affects the stability and accuracy of the RTHS. In this study, a Kalman filter-based adaptive delay compensation (KF-ADC) method is proposed to address this issue. In this novel method, the control plant is represented by a discrete-time model, whose coefficients are time-varying and are estimated online by the KF using the displacement commands and measurements. Based on this time-varying model, the delay compensator is constructed employing the desired displacements. The KF performance is investigated theoretically and numerically. To assess the performance of the proposed strategy, a series of virtual RTHSs are performed on the Benchmark problem in RTHS, which was based on an actual experimental system. Meanwhile, several promising delay-compensation strategies are employed for comparison. Results reveal that the proposed time-delay compensation method effectively enhances the accuracy, stability, and robustness of RTHS.

show abstract

“…In order to accurately reproduce the boundary conditions, numerous efforts have been paid and great progress has been made. To be specific, a polynomial extrapolation (PE) method based on a constant delay assumption was proposed and improved [8][9][10][11]; various adaptive strategies for compensating variable delay have been conceived based on online delay estimation [10,12,13], synchronization error [14][15][16], adaptive inverse control [17][18][19], updated discrete models of the testing system [20,21], and other techniques [22][23][24]. Additionally, sophisticated control strategies, such as robustness control [25][26][27] and nonlinear control [28,29], have also drawn considerable attention in recent years for addressing displacement tracking and delay compensation problems.…”

Section: Introductionmentioning

confidence: 99%

Test Verification of Two-Stage Adaptive Delay Compensation Method for Real-Time Hybrid Simulation

Wang

Yan

Ning

et al. 2020

Shock and Vibration

Self Cite

View full text Add to dashboard Cite

Real-time hybrid simulation (RTHS) is a versatile testing technique for performance evaluation of structures subjected to dynamic excitations. Research revealed that compensation for the delay induced by the dynamics of the loading system and other factors is a critical issue for obtaining reliable test results. Lately, a two-stage adaptive delay compensation (TADC) method was conceived and performed on the benchmark problem of RTHS. For this method, the main part of the system delay is coarsely compensated by the classic polynomial extrapolation (PE) method; the second stage represents a fine remedy for the remaining delay with adaptive compensation based on a discrete model of the loading system. As an extension of this study, this paper aims to further verify and reveal the performance of this method through real tests on a viscous damper specimen. In particular, loading tests with a swept signal and RTHS with sinusoidal and seismic excitations were carried out. Investigations show that the TADC method is endowed with smaller parameter variation ranges, simple yet effective initialization or a soft-start process, less dependence on initial parameter estimation accuracy, and best compensation performance.

show abstract

Adaptive Feedforward and Feedback Compensation Method for Real-time Hybrid Simulation Based on a Discrete Physical Testing System Model

Cited by 20 publications

References 29 publications

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

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

Kalman Filter-Based Adaptive Delay Compensation for Benchmark Problem in Real-Time Hybrid Simulation

Test Verification of Two-Stage Adaptive Delay Compensation Method for Real-Time Hybrid Simulation

Contact Info

Product

Resources

About