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The convergence of stochastic gradient iterative modal identification algorithm and application in modal analysis for space large manipulator

机译:随机梯度迭代模态识别算法的融合与空间大型机械手模态分析中的应用

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Nowadays the light constructors are widely used in the space large manipulators, and some properties of this kind of structure, such as low-frequency, large-range and durable vibration, have raise great attentions in aerospace area. In the paper we applied the wireless sensors networks (WSN) into the modal analysis of manipulators and proposed an interactive modal parameters identification method - SGIMID algorithm. This algorithm uses the stochastic gradient vector to improve interactive modal parameters identification (IMID) method, while the packet transmission mode between the modals is same to IMID algorithm. For the completeness of the proposed algorithm, we present the convergence proof of SGIMIDM. At last, we make applied SGIMID algorithm into the stiffness and frequencies coefficients identification of multi-DOF space manipulators. The experiment results show that, compared with IMID algorithm, the sensor nodes in SGIMID algorithm converge to the synchronous identified result with faster computing speed and less calculating burden.
机译:如今,光构造函数广泛用于空间大型机械手,以及这种结构的一些性质,如低频,大范围和耐用的振动,在航空航天区提高了巨大的关注。在本文中,我们将无线传感器网络(WSN)应用于操纵器的模态分析,提出了一种交互式模态参数识别方法 - SGIMID算法。该算法使用随机梯度向量来改善交互式模态参数识别(IMID)方法,而模块之间的分组传输模式与IMID算法相同。为了提出算法的完整性,我们介绍了Sgimidm的融合证明。最后,我们将SGIMID算法应用于多DOF空间机械手的刚度和频率系数识别。实验结果表明,与IMID算法相比,SGIMID算法中的传感器节点会聚到具有更快的计算速度和更少计算负担的同步所识别的结果。

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