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

TL;DR

This paper introduces a self-tuning state estimation method for adaptive structures using a combination of strain gauges and camera-based measurements, employing sensor fusion and optimal placement to enhance accuracy and flexibility.

Contribution

It presents a novel sensor fusion approach with self-tuning capabilities for adaptive structures, integrating camera and strain sensors with out-of-sequence updates and optimal sensor placement.

Findings

Effective sensor fusion with Kalman filter improves deformation estimation.

Self-tuning algorithm reduces model-structure discrepancy.

Experimental validation on a laboratory adaptive high-rise model.

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.