Self-tuning state estimation for adaptive truss structures using strain gauges and camera-based position measurements. (September 2020)

• Record Type: Journal Article
• Authors: Warsewa, Alexander; Böhm, Michael; Guerra, Flavio; Wagner, Julia L; Haist, Tobias; Tarín, Cristina; Sawodny, Oliver
• Is Part Of: Mechanical systems and signal processing. Volume 143(2020)
• Publication Date: 2020-09
• Subjects: Adaptive structures -- Structural dynamics -- State estimation -- Sensor fusion -- Sensor placement -- Application; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.106822
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Real-time sensor fusion for adaptive structures with consideration of delays. Monitoring of nodal position by optical tracking of light emitters attached to the façade. Self-tuning algorithm for model parameter update and filter adaptation. Practical experiments with realistic excitations on a scaled model of an adaptive high-rise. 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.