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Multi-criterion dynamic structural control using damage detection and distributed active actuators-based LQR controller

delete2025-03-01
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PRE
AI
M
Mahdi Sadeghi
P
Peyman Torkzadeh *
S
Saeed Shojaee
DOI:10.1016/j.istruc.2025.108435delete
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摘要

摘要

En 中文
Investigating the behavior of tall structures, including considering big output data, control of dynamic responses, and detecting structural damage, is one of the most important challenges for scientists. Active control of the structure using existing algorithms, identifying the quantity and location of damage, reducing the order of the model, and estimating the response of the structure under seismic excitations are some of the most important proceedings in the field of controlling and identifying the behavior and characteristics of tall structures. In this study, optimal active control using the Linear Quadratic Regulator algorithm and linear actuators as control devices in combination with damage detection using the impulse responses method in the discrete space, model order reduction of the system using the Singular Value Decomposition technique to improve the speed and convergence of the system analysis process, and predicting the response of the structure using the Observer Kalman Filter Identification have been investigated to control and decrease the dynamic responses and damages respectively. Also, in this study, a numerical model of a 40-story shear structure with uniform distribution of mass and stiffness in the height of the structure is considered, and the damage scenarios are pre-defined as a reduction in story lateral stiffness. The numerical analysis from the current study indicates that the proposed combination method effectively controls the structure's dynamic responses and mitigates damages caused by external seismic forces.
Keyword:
Smart structure
Damage detection
System identification
Kalman filter
Model order reduction
Active control

期刊

Structures 封面图
Structures
IF:
4.3
论文数:
1.3W
被引数:
2.7W

机构

S
shahid bahonar university of kerman (sbuk)
学者数:
3.2K
论文数: 3.0K
被引数: 0
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