techniques, for structural vibration suppression under earthquakes. Various control strategies have been developed to protect structures from natural hazards and improve the comfort of occupants in buildings. However, there has been little development of adaptive building control with the integration of real-time system identification and control design. Generalized predictive control, which combines the process of real-time system identification and the process of predictive control design, has received widespread acceptance and has been successfully applied to various test-beds. This paper presents a formulation of the predictive control scheme for adaptive vibration control of structures under earthquakes. Comprehensive simulations are performed to demonstrate and validate the proposed adaptive control technique for earthquake-induced vibration of a building.
KEYWORDS: Data modeling, Chemical elements, Error analysis, Performance modeling, Point spread functions, Control systems, System identification, Autoregressive models, Structural health monitoring, Computing systems
This paper presents a real-time structural damage identification method for aircraft with flight condition variations.
The proposed approach begins by identifying the dynamic models under various test conditions from time-domain
input/output data. A singular value decomposition technique is then used to characterize and quantify the parameter
uncertainties from the identified models. The uncertainty coordinates, corresponding to the identified principal
directions, of the identified models are computed, and the residual errors between the identified uncertainty
coordinates and the estimated uncertainty coordinates of the health structure are used to identify damage status. A
correlation approach is applied to identify damage type and intensity, based on the difference between the identified
parameters and the estimated parameters of the healthy structure. The proposed approach is demonstrated by
application to the Benchmark Active Controls Technology (BACT) wind-tunnel model.
KEYWORDS: Principal component analysis, Feedback control, Structural health monitoring, Damage detection, Pattern recognition, Systems modeling, System identification, Finite element methods, Device simulation, Data modeling
This paper presents an innovative technique for structural damage detection that is based on
principal component analysis when feedback controllers are incorporated into the structure. The
use of feedback control can generate additional modal parameters of closed-loop systems and also
enhance the sensitivity of modal parameters to structural damage. Principal component analysis
(PCA) is used to extract the features of parameter changes due to damage for open-loop and
closed-loop systems. The effect of uncertainty, such as measurement noise, on damage
identification is studied based on PCA. The objective of this research is to develop
methodologies, based on feedback control with PCA, to improve structural damage identification
under model uncertainty.
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