Abstract In the context of control of smart structures, we present an approach for state estimation of adaptive buildings with active load-bearing elements. For obtaining information on structural deformation, a system composed of a digital camera and optical emitters affixed to selected nodal points is introduced as a complement to conventional strain gauge sensors. Sensor fusion for this novel combination of sensors is carried out using a Kalman filter that operates on a reduced-order structure model obtained by modal analysis. Signal delay caused by image processing is compensated for by an out-of-sequence measurement update which provides for a flexible and modular estimation algorithm. Since the camera system is very precise, a self-tuning algorithm that adjusts model along with observer parameters is introduced to reduce discrepancy between system dynamic model and actual structural behavior. We further employ optimal sensor placement to limit the number of sensors to be placed on a given structure and examine the impact on estimation accuracy. A laboratory scale model of an adaptive high-rise with actuated columns and diagonal bracings is used for experimental demonstration of the proposed estimation scheme.

中文翻译：

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使用应变仪和基于相机的位置测量的自适应桁架结构自调整状态估计

摘要 在智能结构控制的背景下，我们提出了一种具有主动承载元件的自适应建筑物的状态估计方法。为了获得结构变形的信息，引入了一个由数码相机和固定在选定节点上的光学发射器组成的系统，作为对传统应变计传感器的补充。这种新型传感器组合的传感器融合是使用卡尔曼滤波器进行的，该滤波器对通过模态分析获得的降阶结构模型进行操作。由图像处理引起的信号延迟通过提供灵活和模块化估计算法的失序测量更新来补偿。由于相机系统非常精确，引入了一种随观测器参数调整模型的自调整算法，以减少系统动态模型与实际结构行为之间的差异。我们进一步采用最佳传感器放置来限制要放置在给定结构上的传感器数量，并检查对估计精度的影响。具有致动柱和对角支撑的自适应高层的实验室比例模型用于所提出的估计方案的实验演示。