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Real-time hybrid simulation (RTHS) is recently applied to study the vibration of the vehicle–bridge coupling system, where bridges and the vehicle are the numerical and experimental substructures respectively. Inevitable time delay of the boundary loading imposed to the experimental substructure will greatly affect the accuracy and stability of RTHS. To minimize the time delay effects, this paper proposes a recursive prediction optimal (RPO) compensator based on optimal control and recursive least squares. The numerical simulation of the RTHS of the vehicle–bridge coupling system is conducted using MATLAB/Simulink. The performance of the RPO method is compared with three traditional time delay compensation methods (i.e. the polynomial extrapolation (NI), the inverse compensation (IC) and the derivative feedforward (DF)) through both open-loop and RTHS simulations. The RPO method shows the best compensation performance in the frequency range up to 20[Formula: see text]Hz.

Real-time hybrid simulation (RTHS) is a practical, cost-effective, and versatile experimental technique to evaluate structural performance under dynamic excitation. The simulated structure is commonly split into a physically tested rate-dependent substructure (PS) and a numerically simulated substructure (NS). A transfer system such as a servo-hydraulic actuator is used to impose boundary conditions on the PS. Consequently, efficient actuator control is necessary to guarantee reliable simulation results. However, time delay and uncertainties exist due to the dynamics of the actuator, which adversely influence the accuracy and stability of RTHS. Therefore, an innovative robust actuator dynamics compensation method is proposed in this study comprising three components, namely a mixed sensitivity-based robust H∞ controller to stabilize the actuator–specimen dynamics, a polynomial extrapolation module to further cancel the actuator delay, and an adaptive filter for displacement reconstruction of the actuator–specimen system. A detailed design procedure of the proposed strategy is presented. The efficacy of the proposed strategy is validated through a series of virtual tests on the benchmark problem for RTHS. Results show that the proposed method exhibits excellent tracking performance and robustness. In particular, the maximum values of the calculated time delay (TD), root mean square of the tracking error (RMSE), and peak tracking error (PE) are 0 ms, 2.56%, and 2.12%, respectively, whereas the maximum values of the standard deviation of TD, RMSE, and PE are 0 ms, 0.25%, and 0.33%, respectively.

During a real-time hybrid simulation (RTHS), inevitable time delay of actuators when responding to a command will reduce the accuracy of test results and sometimes even cause unstable testing. The inner-loop controller of an actuator is generally capable of eliminating the effects due to small time-delays. However, if a test specimen behaves nonlinearly, accuracy of RTHS results will be impaired. In addition to the uncertainty of test specimens and transfer system, measurement noises of the displacement and force sensors also require a robust external controller for RTHS. In this paper, a robust linear-quadratic-gaussian (LQG) controller with a Loop Transfer Recovery (LTR) procedure and a polynomial-based feedforward prediction (FP) algorithm is proposed to compensate the adverse effects due to time delay and uncertainties within the RTHS testing system. The stability and robustness of the proposed controller are analysed in the frequency domain using the Nyquist curve and the Bode diagrams. Numerical simulations are then carried out on the benchmark problem using both the proposed robust and the conventional LQG controllers and their performance is compared using the nine evaluation criteria. It is demonstrated that the robust LQG (RLQG) controller outperforms the conventional LQG controller in terms of compensating the parameter uncertainties in the testing system and achieving accurate RTHS results.

Benchmark problem for real-time hybrid simulation (RTHS) is proposed to enable researchers assessing the robustness and performance of various tracking controllers in a uniform setting. In this paper, a controller combining the sliding mode controller (SMC) and an improved adaptive polynomial-based forward prediction (IAFP) algorithm is proposed. The SMC is adopted for its robustness to the uncertainties that may be experienced in an RTHS testing; while the IAFP is employed as a time delay compensator to stabilize RTHS with large time delay and reduce the time delay effects. Numerical simulations and physical experiments of the RTHS on a linear test specimen are conducted utilizing the program available through the benchmark problem. Time history responses obtained from RTHS are compared with those of the reference model and the nine evaluation criteria are computed, from which the robustness of the proposed controller and accurate RTHS results are demonstrated.

Real-time hybrid simulation (RTHS) is an innovative and versatile technique for evaluating the dynamic responses of structural and mechanical systems. This technique separates the emulated system into numerical and physical substructures, which are analyzed by computers and loaded in laboratories, respectively. Ensuring the boundary conditions between the two substructures through a transfer system plays a significant role in obtaining reliable and accurate testing results. However, measurement noise and the delay between commands and responses due to the dynamic performance of the transfer system are inevitable in RTHS. To address these issues and to achieve outstanding tracking performance and excellent robustness, this paper proposes an adaptive Kalman-based noise filter and an adaptive two-stage delay compensation method. In particular, in the novel noise filter strategy, adaptive inverse compensation with parameters updated by the least squares method is adopted to accommodate the amplitude and phase errors induced by a traditional Kalman filter. In the proposed delay compensation method, classic polynomial extrapolation and an adaptive inverse strategy are employed for coarse and fine compensation, respectively. Virtual RTHS on a benchmark problem reveals the satisfactory tracking performance and robustness of the proposed methods. Comparisons with polynomial extrapolation and single-stage adaptive compensation indicate the superiority of the proposed two-stage delay compensation method.