1993 Fiscal Year Final Research Report Summary
Studies on structural identification and inteligent control system of seismic response
| Project/Area Number |
04650406
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
土木構造
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| Research Institution | Tottori University |
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Principal Investigator |
NODA Shigeru Tottori University, Faculty of Engineering, Associate Professor, 工学部, 助教授 (80135532)
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| Project Period (FY) |
1992 – 1993
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| Keywords | Inteigent control system / Structural identification / Seismic response / System dynamics / Neural network / Active control / Stochastic system theory / Nonlinear cntrol |
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| Research Abstract |
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
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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
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