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Parameter identification in explicit structural dynamics: performance of the extended Kalman filter

机译:显式结构动力学中的参数识别:扩展卡尔曼滤波器的性能

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摘要

An extended Kalman filter (EKF) approach is adopted in this paper for structural systems subject to dynamic loadings to simultaneously estimate the state and calibrate constitutive parameters. To pursue this aim, after space and time discretizations a joint system state vector is introduced, gathering nodal displacements and constitutive parameters to be identified. Because of the linearization of the discretized equations governing filter updates, the EKF can lead to inaccurate model calibration. It is shown that, even though the state of the system is always followed with a high level of accuracy, unsatisfactory parameter estimations can be obtained in the case of degrading strength of the structure, that is in the case of softening. Both single degree-of-freedom (DOF) and multi-DOF dynamic systems are analyzed to detect the possible sources of this inaccuracy.
机译:本文将扩展卡尔曼滤波器(EKF)方法用于承受动态载荷的结构系统,以同时估计状态和校准本构参数。为了实现这一目标,在空间和时间离散化之后,引入了一个联合系统状态向量,收集节点位移和本构参数进行识别。由于控制滤波器更新的离散方程的线性化,EKF可能导致模型校准不准确。结果表明,即使总是高度精确地跟踪系统的状态,在结构强度下降的情况下,即在软化的情况下,也可能获得不令人满意的参数估计。分析了单自由度(DOF)和多自由度动态系统,以检测这种不精确性的可能来源。

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