Real‐time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost‐effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an adaptive compensation method based on a conditional adaptive time series (CATS) compensator and a long short‐term memory (LSTM) network, termed CATS‐LSTM. The LSTM model predicts actuator responses for parameter estimation and calculates prediction errors, improving control performance and reducing delays. The effectiveness of the proposed CATS‐LSTM method and the accuracy of the LSTM prediction are validated through a series of simulations and experiments. The results indicate that the proposed CATS‐LSTM method outperforms both the CATS and phase lead (PL) methods. Compared to the CATS method, the proposed method reduces the maximum delay, root mean square error, and peak error by 3 ms, 3.66%, and 4.78%, respectively, while achieving reductions of 12 ms, 8.4%, and 10.05%, compared to the PL method. Furthermore, the CATS‐LSTM method is significantly less sensitive to initial parameter estimates, compared to the CATS method, enhancing robustness and mitigating the effects of inaccurate or varying initial parameter estimates.

中文翻译：

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使用长短期记忆网络进行自适应补偿，以提高实时混合仿真中的控制性能


实时混合仿真 （RTHS） 将结构系统分为数值和实验子结构，为分析结构系统（尤其是大型或复杂的结构系统）提供了一种经济高效的解决方案。然而，这些子结构之间的驱动系统不可避免地会引入延迟，从而影响 RTHS 的稳定性和准确性。为了解决这个问题，本研究提出了一种基于条件自适应时间序列 （CATS） 补偿器和长短期记忆 （LSTM） 网络的自适应补偿方法，称为 CATS-LSTM。LSTM 模型预测执行器响应以进行参数估计并计算预测误差，从而提高控制性能并减少延迟。通过一系列仿真和实验验证了所提出的 CATS-LSTM 方法的有效性和 LSTM 预测的准确性。结果表明，所提出的 CATS-LSTM 方法优于 CATS 和相位超前 （PL） 方法。与 CATS 方法相比，所提方法的最大延迟、均方根误差和峰值误差分别降低了 3 ms、3.66% 和 4.78%，同时与 PL 方法相比减少了 12 ms、8.4% 和 10.05%。此外，与 CATS 方法相比，CATS-LSTM 方法对初始参数估计的敏感性明显较低，从而提高了稳健性并减轻了不准确或变化的初始参数估计的影响。