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Control‐oriented system identification using ERA
Author(s) -
Giraldo Diego,
Yoshida Osamu,
Dyke Shirley J.,
Giacosa Luca
Publication year - 2004
Publication title -
structural control and health monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.587
H-Index - 62
eISSN - 1545-2263
pISSN - 1545-2255
DOI - 10.1002/stc.46
Subject(s) - identification (biology) , modal , realization (probability) , system identification , control engineering , control (management) , engineering , stability (learning theory) , control system , earthquake engineering , computer science , artificial intelligence , data modeling , machine learning , structural engineering , software engineering , mathematics , chemistry , botany , polymer chemistry , biology , statistics , electrical engineering
Abstract One of the most critical steps when designing a control system for a civil structure can be the construction of a mathematical model that can accurately reproduce the response of the real system to disturbances and control forces. The identification of such models for use in active and semiactive seismic control often presents a challenge to the designer, but is important owing to its impact on the performance and stability of the resulting control system. In this paper, two system identification approaches suitable for developing linear, control‐oriented models for full‐scale civil engineering structures are proposed, implemented and compared. The eigensystem realization algorithm is used for modal identification in both approaches. Experimental implementation and verification is performed using data collected from two experimental structures, each portraying different challenging features. Experimental verification of the outcomes indicates that both methods result in effective control‐oriented models. Copyright © 2004 John Wiley & Sons, Ltd.