Magnetorheological (MR) damper is efficient to mitigate vibration of the structure subject to severe excitations. The identification of nonlinear characteristics of MR damper has attracted increasing attention. However, it is still challenging to identify model‐free hysteresis of MR dampers embedded in structures using only incomplete measurements of structural responses under unknown excitations. In this paper, an identification technique is proposed for this tough task, namely, nonparametric identification of MR nonlinear restoring forces in building structures under unknown excitations. The proposed technique involves identifications in two stages. In the first stage, the identification of linear parameters of bare structure and MR dampers under low‐level unknown excitations is conducted based on the generalized extended Kalman filter with unknown input (GEKF‐UI) by the authors. In the second stage, the hysteretic forces of MR dampers are proposed to be treated as “unknown fictitious forces” to the corresponding linear bare structure identified in the first stage. The generalized Kalman filter with unknown inputs (GKF‐UI) by the authors is adopted to identify all unknown inputs including the “unknown fictitious forces” originated from MR dampers. To demonstrate the proposed technique, some numerical examples are used to identify model‐free hysteresis of single or multi MR dampers with different nonlinear restoring force models in shear frames under unknown external force excitations or unknown seismic excitations, respectively. Furthermore, experimental testing of the identification of model‐free MR damper in a multi‐story shear frame under unknown external excitation is conducted.

中文翻译：

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使用不完整的结构响应识别未知激励下嵌入建筑物的磁流变阻尼器的无模型滞后力

磁流变（MR）阻尼器可有效缓解受到强烈激励的结构的振动。MR阻尼器非线性特性的识别引起了越来越多的关注。但是，仅通过在未知激励下对结构响应的不完整测量来确定嵌在结构中的MR阻尼器的无模型滞后仍然具有挑战性。针对这种艰巨的任务，本文提出了一种识别技术，即在未知激励下对建筑结构中的MR非线性恢复力进行非参数识别。所提出的技术涉及两个阶段的识别。在第一阶段 作者基于广义输入未知输入的扩展卡尔曼滤波器（GEKF‐UI），对低水平未知激励下的裸露结构和MR阻尼器的线性参数进行了识别。在第二阶段中，建议将MR阻尼器的滞后力视为在第一阶段中识别出的相应线性裸结构的“未知虚拟力”。作者采用了带有未知输入的广义卡尔曼滤波器（GKF‐UI）来识别所有未知输入，包括源自MR阻尼器的“未知虚拟力”。为了演示提出的技术，一些数值示例分别用于在未知外力激励或未知地震激励下，在剪切框架中具有不同非线性恢复力模型的单个或多个MR阻尼器的无模型滞后。此外，还进行了在未知外部激励下多层剪切框架中无模型MR阻尼器识别的实验测试。