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Force identification under unknown initial conditions by using concomitant mapping matrix and sparse regularization

机译:通过使用伴随的映射矩阵和稀疏正则化在未知初始条件下强制识别

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摘要

Unknown initial conditions can affect the identified accuracy of dynamic forces. Direct measurement of initial conditions is relatively difficult. This study proposes a sparse regularization-based method for identifying forces considering influences of unknown initial conditions. The initial conditions are embedded in a classical governing equation of force identification. The key idea is to introduce a concept of concomitant mapping matrix for reasonably expressing the initial conditions. First, a dictionary is introduced for expanding the dynamic forces. Then, the concomitant mapping matrix is formulated by using free vibrating responses, which correspond to structural responses happening after the structure is subjected to each atom of the force dictionary. A sparse regularization strategy is applied for solving the ill-conditioned equation. After that, the problem of force identification is converted into an optimization problem, and it can be solved by using a one-step strategy. Numerical simulations are carried out for verifying the feasibility and effectiveness of the proposed method. Illustrated results clearly show the applicability and robustness of the proposed method for dealing with force reconstruction and moving force identification.
机译:未知的初始条件可能会影响动态力的识别精度。直接测量初始条件相对困难。本研究提出了一种基于稀疏正则化的力识别方法,该方法考虑了未知初始条件的影响。初始条件嵌入在力识别的经典控制方程中。其核心思想是引入伴随映射矩阵的概念,以合理地表达初始条件。首先,引入字典来扩展动力。然后,通过使用自由振动响应(对应于结构受到力字典的每个原子后发生的结构响应)来建立伴随映射矩阵。采用稀疏正则化策略求解病态方程。然后,将力识别问题转化为优化问题,并采用一步策略进行求解。通过数值仿真验证了该方法的可行性和有效性。仿真结果清楚地表明了该方法在力重构和运动力识别中的适用性和鲁棒性。

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