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.

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.

PREDICTING UNKNOWN MOTION FOR MODEL INDEPENDENT VISUAL SERVOING

2001 ◽  
Vol 01 (03) ◽  
pp. 287-302 ◽  
Author(s):  
PATRICE WIRA ◽  
JEAN-PHILIPPE URBAN
Keyword(s):  
Kalman Filter ◽  
Time Delay ◽  
Real Time ◽  
Visual Servoing ◽  
Dimensional Space ◽  
Tracking Error ◽  
State Model ◽  
Divide And Conquer ◽  
Image Feature ◽  
Robot Arm

Prediction in real-time image sequences is a key-feature for visual servoing applications. It is used to compensate for the time-delay introduced by the image feature extraction process in the visual feedback loop. In order to track targets in a three-dimensional space in real-time with a robot arm, the target's movement and the robot end-effector's next position are predicted from the previous movements. A modular prediction architecture is presented, which is based on the Kalman filtering principle. The Kalman filter is an optimal stochastic estimation technique which needs an accurate system model and which is particularly sensitive to noise. The performances of this filter diminish with nonlinear systems and with time-varying environments. Therefore, we propose an adaptive Kalman filter using the modular framework of mixture of experts regulated by a gating network. The proposed filter has an adaptive state model to represent the system around its current state as close as possible. Different realizations of these state model adaptive Kalman filters are organized according to the divide-and-conquer principle: they all participate to the global estimation and a neural network mediates their different outputs in an unsupervised manner and tunes their parameters. The performances of the proposed approach are evaluated in terms of precision, capability to estimate and compensate abrupt changes in targets trajectories, as well as to adapt to time-variant parameters. The experiments prove that, without the use of models (e.g. the camera model, kinematic robot model, and system parameters) and without any prior knowledge about the targets movements, the predictions allow to compensate for the time-delay and to reduce the tracking error.

scholarly journals Mixed Sensitivity-Based Robust H∞ Control Method for Real-Time Hybrid Simulation

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 840
Author(s):  
Xizhan Ning
Keyword(s):  
Real Time ◽  
Control Method ◽  
Weighting Function ◽  
Hybrid Simulation ◽  
Shaking Table ◽  
Transfer System ◽  
Hydraulic Actuator ◽  
Earthquake Excitation ◽  
Engineering Structures ◽  
Weighting Functions

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.

Sign in / Sign up

Export Citation Format

Share Document