Theoretical and experimental studies on critical time delay of multi-DOF real-time hybrid simulation

2021 ◽  
Author(s):  
Lu Liqiao ◽  
Wang Jinting ◽  
Ding Hao ◽  
Zhu Fei
Keyword(s):  
Time Delay ◽  
Real Time ◽  
Experimental Studies ◽  
Hybrid Simulation ◽  
Critical Time

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

2020 ◽  
Vol 10 (20) ◽  
pp. 7101
Author(s):  
Xizhan Ning ◽  
Zhen Wang ◽  
Bin Wu
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Real Time ◽  
Control Plant ◽  
Hybrid Simulation ◽  
Time Varying ◽  
Transfer System ◽  
Benchmark Problem ◽  
Delay Compensation ◽  
Comparison Results

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.

Improving model-based compensation method for real-time hybrid simulation considering error of identified model

2020 ◽  
pp. 107754632096162
Author(s):  
Zihao Zhou ◽  
Ning Li
Keyword(s):  
Time Delay ◽  
Transfer Function ◽  
Real Time ◽  
Hybrid Simulation ◽  
Compensation Method ◽  
Test Results ◽  
Two Stage ◽  
The Real ◽  
Model Based ◽  
Real Plant

Time delay is a critical and unavoidable problem in real-time hybrid simulation. An accurate and effective compensation method for time delay is necessary for the safety of real-time hybrid simulation and the reliability of test results. Generally, a model-based compensation method can be adopted, which is derived from the identified transfer function by assuming the latter can accurately represent the real plant. However, there must be some differences between the transfer function and the real plant. To facilitate the development of real-time hybrid simulation, we proposed a two-stage feedforward compensation method considering the error between the transfer function identified and the real plant. The compensation strategy proposed in this study was not only based on the transfer function but also introduced an error model as a second-stage compensation into a compensator to realize the synchronization of command and measurement. To verify the efficiency of the proposed method, comparisons in time domain and frequency domain with the feedforward compensator in a model-based feedforward–feedback control method were carried out. Compared with the feedforward compensator, the two-stage method achieved better tracking performance, especially in the high-frequency bandwidth. The test results verified that for a band-limited white noise of 0–30 Hz, the phase lag of the actuation system can be limited to ±5°. Finally, the two-stage method was applied to a real-time hybrid simulation of a two-story frame to illustrate its compensation effect on time delay.

Implementation and application of the unconditionally stable explicit parametrically dissipative KR-αmethod for real-time hybrid simulation

2014 ◽  
Vol 44 (5) ◽  
pp. 735-755 ◽  
Author(s):  
Chinmoy Kolay ◽  
James M. Ricles ◽  
Thomas M. Marullo ◽  
Akbar Mahvashmohammadi ◽  
Richard Sause
Keyword(s):  
Real Time ◽  
Hybrid Simulation ◽  
Unconditionally Stable

Multi-rate Real Time Hybrid Simulation operated on a flexible LabVIEW real-time platform

2021 ◽  
Vol 239 ◽  
pp. 112308
Author(s):  
Jacob P. Waldbjoern ◽  
Amin Maghareh ◽  
Ge Ou ◽  
Shirley J. Dyke ◽  
Henrik Stang
Keyword(s):  
Real Time ◽  
Hybrid Simulation

scholarly journals An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay

2020 ◽  
Author(s):  
Barbara Barros Carlos ◽  
Tommaso Sartor ◽  
Andrea Zanelli ◽  
Gianluca Frison ◽  
Wolfram Burgard ◽  
...  
Keyword(s):  
Time Delay ◽  
Real Time ◽  
Position Control ◽  
Communication Time

Stability of real-time hybrid simulation involving time-varying delay and direct integration algorithms

2021 ◽  
pp. 107754632110016
Author(s):  
Liang Huang ◽  
Cheng Chen ◽  
Shenjiang Huang ◽  
Jingfeng Wang
Keyword(s):  
Real Time ◽  
Discrete Time ◽  
Continuous Time ◽  
Hybrid Simulation ◽  
Time Varying ◽  
Discrete Time System ◽  
Time System ◽  
Integration Algorithms ◽  
The Stability ◽  
Continuous Time System

Stability presents a critical issue for real-time hybrid simulation. Actuator delay might destabilize the real-time test without proper compensation. Previous research often assumed real-time hybrid simulation as a continuous-time system; however, it is more appropriately treated as a discrete-time system because of application of digital devices and integration algorithms. By using the Lyapunov–Krasovskii theory, this study explores the convoluted effect of integration algorithms and actuator delay on the stability of real-time hybrid simulation. Both theoretical and numerical analysis results demonstrate that (1) the direct integration algorithm is preferably used for real-time hybrid simulation because of its computational efficiency; (2) the stability analysis of real-time hybrid simulation highly depends on actuator delay models, and the actuator model that accounts for time-varying characteristic will lead to more conservative stability; and (3) the integration step is constrained by the algorithm and structural frequencies. Moreover, when the step is small, the stability of the discrete-time system will approach that of the corresponding continuous-time system. The study establishes a bridge between continuous- and discrete-time systems for stability analysis of real-time hybrid simulation.

scholarly journals Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaghoub Dabiri ◽  
Alex Van der Velden ◽  
Kevin L. Sack ◽  
Jenny S. Choy ◽  
Julius M. Guccione ◽  
...  
Keyword(s):  
Real Time ◽  
Material Properties ◽  
In Silico ◽  
Prediction Models ◽  
Heart Function ◽  
Active Material ◽  
Experimental Studies ◽  
Left Ventricular ◽  
Feed Forward ◽  
Ventricular Mechanics

AbstractAn understanding of left ventricle (LV) mechanics is fundamental for designing better preventive, diagnostic, and treatment strategies for improved heart function. Because of the costs of clinical and experimental studies to treat and understand heart function, respectively, in-silico models play an important role. Finite element (FE) models, which have been used to create in-silico LV models for different cardiac health and disease conditions, as well as cardiac device design, are time-consuming and require powerful computational resources, which limits their use when real-time results are needed. As an alternative, we sought to use deep learning (DL) for LV in-silico modeling. We used 80 four-chamber heart FE models for feed forward, as well as recurrent neural network (RNN) with long short-term memory (LSTM) models for LV pressure and volume. We used 120 LV-only FE models for training LV stress predictions. The active material properties of the myocardium and time were features for the LV pressure and volume training, and passive material properties and element centroid coordinates were features of the LV stress prediction models. For six test FE models, the DL error for LV volume was 1.599 ± 1.227 ml, and the error for pressure was 1.257 ± 0.488 mmHg; for 20 LV FE test examples, the mean absolute errors were, respectively, 0.179 ± 0.050 for myofiber, 0.049 ± 0.017 for cross-fiber, and 0.039 ± 0.011 kPa for shear stress. After training, the DL runtime was in the order of seconds whereas equivalent FE runtime was in the order of several hours (pressure and volume) or 20 min (stress). We conclude that using DL, LV in-silico simulations can be provided for applications requiring real-time results.

A self-oscillating gel system with complex dynamic behavior based on a time delay between the oscillations

Soft Matter ◽  
2021 ◽  
Author(s):  
Xiuchen Li ◽  
Jie Li ◽  
Zhaohui Zheng ◽  
Jinni Deng ◽  
Yi Pan ◽  
...  
Keyword(s):  
Time Delay ◽  
Dynamic Behavior ◽  
Experimental Studies ◽  
Complex Dynamic ◽  
Chemical Oscillation ◽  
Mechanical Oscillation ◽  
Behavior Based ◽  
Gel System

The time delay existing between the chemical oscillation and mechanical oscillation (C-M delay) in a self-oscillating gel (SOG) system is observable in previous experimental studies. However, how the C-M delay...

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