Optimal neurocontroller for nonlinear benchmark structure
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Title
- Optimal neurocontroller for nonlinear benchmark structure
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Author(s)
- Kim, DH; Seo, SN; Lee, IW
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Alternative Author(s)
- 김동현; 서승남
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Publication Year
- 2004-04
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Abstract
- A neurocontrol method is applied to the nonlinear benchmark control problem. A neurocontroller is trained based on a reduced-order linear design model, then it is used to control a nonlinear evaluation model. In training the controller, a sensitivity evaluation scheme is used and weights are updated by minimizing a cost function. Absolute accelerations directly measured from sensors are used as the feedback signals for the controller. Not only the current step acceleration, but delay signals of sensor readings, are used to enhance the training capability. Numerical examples show that the controlled responses are considerably reduced compared with the uncontrolled case. In conclusion, the possibility. of the proposed control algorithm as a candidate for the controller of nonlinear building is shown.
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ISSN
- 0733-9399
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URI
- https://sciwatch.kiost.ac.kr/handle/2020.kiost/5269
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DOI
- 10.1061/(ASCE)0733-9399(2004)130:4(424)
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Bibliographic Citation
- JOURNAL OF ENGINEERING MECHANICS, v.130, no.4, pp.424 - 429, 2004
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Publisher
- ASCE-AMER SOC CIVIL ENGINEERS
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Subject
- NEURAL-NETWORKS
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Keywords
- neural networks; structural control; nonlinear systems; structural dynamics
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Type
- Article
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Language
- English
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Document Type
- Article
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