Optimal neurocontroller for nonlinear benchmark structure SCIE SCOPUS

Cited 14 time in WEB OF SCIENCE Cited 19 time in Scopus
Title
Optimal neurocontroller for nonlinear benchmark structure
Author(s)
Kim, DH; Seo, SN; Lee, IW
Publication Year
2004-04
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.
ISSN
0733-9399
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/5269
DOI
10.1061/(ASCE)0733-9399(2004)130:4(424)
Bibliographic Citation
JOURNAL OF ENGINEERING MECHANICS, v.130, no.4, pp.424 - 429, 2004
Publisher
ASCE-AMER SOC CIVIL ENGINEERS
Subject
NEURAL-NETWORKS
Keywords
neural networks; structural control; nonlinear systems; structural dynamics
Type
Article
Language
English
Document Type
Article
Publisher
ASCE-AMER SOC CIVIL ENGINEERS
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