High performance compensation using an adaptive strategy for real-time hybrid simulation

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.