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Self-Tuning State Estimation for Adaptive Truss Structures Using Strain Gauges and Camera-Based Position Measurements

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Document pages: 16 pages

Abstract: In the context of control of smart structures, we present an approach forstate estimation of adaptive buildings with active load-bearing elements. Forobtaining information on structural deformation, a system composed of a digitalcamera and optical emitters affixed to selected nodal points is introduced as acomplement to conventional strain gauge sensors. Sensor fusion for this novelcombination of sensors is carried out using a Kalman filter that operates on areduced-order structure model obtained by modal analysis. Signal delay causedby image processing is compensated for by an out-of-sequence measurement updatewhich provides for a flexible and modular estimation algorithm. Since thecamera system is very precise, a self-tuning algorithm that adjusts model alongwith observer parameters is introduced to reduce discrepancy between systemdynamic model and actual structural behavior. We further employ optimal sensorplacement to limit the number of sensors to be placed on a given structure andexamine the impact on estimation accuracy. A laboratory scale model of anadaptive high-rise with actuated columns and diagonal bracings is used forexperimental demonstration of the proposed estimation scheme.

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