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Studies on structural identification and inteligent control system of seismic response

结构地震反应识别与智能控制系统研究

基本信息

批准号：
04650406
负责人：
NODA Shigeru
金额：
$ 1.28万
依托单位：
Tottori University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1992
资助国家：
日本
起止时间：
1992 至 1993
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-04650406/
关键词：
Inteigent control system Structural identification Seismic response System dynamics Neural network Active control Stochastic system theory Nonlinear cntrol ダメージコントロール制震地震動モニタリングシステム

项目摘要

The main results of this study may be summarized as follows :1.A metod was presented to identify the structural parameters on a hysteretic and degrading model for multidegree of fredom structure by the Extended Kalman filter (the EK-WGI method). Numerical results show that the present method predicts the linear and nonlinear behavior of the structure vary well even for the case withsevere nnlinearity. However, the estiated parameters are not quite satisfactory, particulary for inelastic parameters which are insensitive to the structura respnse.2.A undating technique to autoassociate by learning a concerned dynamic system, which is represented by a system dynamics odel, was proposed. To justify the ogical problem, a numerical test was carried out for identification of a SDOF linear system. In example in which the skelton of a SD mode is precise, stable idetification was made on a linear SDOF system. However, in example in which a ore complicated SD model is employed, it was observed tha … More t values of weights n each link tend to converge other vaues than exected or even diverge, althugh level components are identified properly.3.It seems that some mathematical device needs to be integrated into the SD model. As this regard, neural network's atoassociate techniques might serve as the means. In order to obtain a regularly directed learning, effective use of the structural learng was cnsidered as an alternative methd for parmeter and system identificatin problems. A global and local iteratin procedure was proosed to obtan stable ad fast cnvergency in the learning of recurrent networks.4.A method to synthesize an ptimal controller by using adaptive control theory was presented. The design procedure of the ptimum control consists of an identifiation of the structure and a synthesis of the controller The iterated linear filter-smoother with weighted local iteration (WILFS) was proposed to use in the estimation of the structural parameters. The otimal controller was easy to be synthesized based on the hysical model identified by this algorith. By carrying out comuter simulations, the usefulness of WILFS method for the optimum control was veified. Less

1.提出了一种基于扩展卡尔曼滤波的多自由度结构滞回退化模型参数识别方法（EK-WGI方法）。数值结果表明，即使在严重非线性的情况下，该方法也能很好地预测结构的线性和非线性行为。然而，所估计的参数并不十分令人满意，尤其是对于对结构响应不敏感的非弹性参数。2.提出了一种通过学习相关动力学系统（用系统动力学模型表示）来实现自联想的方法。为了证明逻辑问题的合理性，对单自由度线性系统的辨识进行了数值试验。以SD模态的稳定性为例，对线性单自由度系统进行了稳定性判别。然而，在采用矿石复杂SD模型的实例中，观察到 ...更多信息尽管水平分量被正确识别，但每个环节的权重值往往会收敛于期望值之外，甚至发散。在这方面，神经网络的自联想技术可以作为一种手段。为了获得有规律的定向学习，有效地利用结构学习被认为是参数和系统辨识问题的一种替代方法。提出了一种全局和局部迭代的方法，使递归网络在学习过程中获得稳定和快速的收敛性。4.提出了一种利用自适应控制理论综合最优控制器的方法。最优控制的设计过程包括结构的辨识和控制器的综合。提出了加权局部迭代迭代线性滤波平滑器（WILFS）用于结构参数的估计。该算法易于根据辨识的物理模型综合最优控制器。通过计算机仿真，验证了WILFS方法在最优控制中的有效性。少