Neuro-genetic algorithm for non-linear active control of structures

In a companion paper, a new non‐linear control model was presented for active control of three‐dimensional (3D) building structures including geometrical and material non‐linearities, coupling action between lateral and torsional motions, and actuator dynamics (Int. J. Numer. Meth. Engng; DOI: 10.10...

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Vydané v:International journal for numerical methods in engineering Ročník 75; číslo 7; s. 770 - 786
Hlavní autori: Jiang, Xiaomo, Adeli, Hojjat
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Chichester, UK John Wiley & Sons, Ltd 13.08.2008
Wiley
Predmet:
Applied sciences
Buildings. Public works
Computational techniques
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
genetic algorithm
Geotechnics
high-rise building
Mathematical methods in physics
neuroemulator
non-linear active control
Physics
Solid mechanics
Structural and continuum mechanics
Structure-soil interaction
Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...)
wavelet neural network
Metallic structure
Non linear material
Rotating machine
Non linear control
Neural network
Modeling
wavelet neural network
Fuzzy logic
Geometrical structure
high-rise building
Wavelet transformation
Genetic algorithm
Earthquakes
Optimal control
Vibration control
Force control
Motion control
neuroemulator
non-linear active control
High rise building
Civil engineering
Structural analysis
ISSN:0029-5981, 1097-0207
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Shrnutí:In a companion paper, a new non‐linear control model was presented for active control of three‐dimensional (3D) building structures including geometrical and material non‐linearities, coupling action between lateral and torsional motions, and actuator dynamics (Int. J. Numer. Meth. Engng; DOI: 10.1002/nme.2195). A dynamic fuzzy wavelet neuroemulator was presented for predicting the structural response in future time steps. In this paper, a new neuro‐genetic algorithm or controller is presented for finding the optimal control forces. The control algorithm does not need the pre‐training required in a neural network‐based controller, which improves the efficiency of the general control methodology significantly. Two 3D steel building structures, a 12‐story structure with vertical setbacks and an 8‐story structure with plan irregularity, are used to validate the neuro‐genetic control algorithm under three different seismic excitations. Numerical validations demonstrate that the new control methodology significantly reduces the displacements of buildings subjected to various seismic excitations including structures with plan and elevation irregularities. Copyright © 2008 John Wiley & Sons, Ltd.
Bibliografia:istex:A2B46528322837658627952812E5B30AC29E0822
ArticleID:NME2274
ark:/67375/WNG-8PB33WWG-N
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0029-5981
1097-0207
DOI:10.1002/nme.2274