Obtaining refined first-order predictive models of linear structural systems

Abstract

This study presents an effective method for identifying predictive models and the underlying modal parameters of linear structural systems using only measured output and excitation time histories obtained from dynamic testing. The system under examination is modelled as a first-order multi-input multi-output time-invariant system, and the structural model is realized using the Eigensystem Realization Algorithm together with the Observer/Kalman filter IDentification algorithm. The identified state-space model is further refined using a non-linear optimization technique based on sequential quadratic programming. The numerical examples show that the developed methodology performs very well even in the presence of inadequate instrumentation and measurement noise, and that the methodology is highly capable of creating realistic predictive models of structural systems, as well as estimating their underlying modal parameters. Copyright © 2002 John Wiley and Sons, Ltd.