Open AccessBook
Subspace Identification for Linear Systems: Theory - Implementation - Applications
Peter Van Overschee,Bart De Moor +1 more
TLDR
This book focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finitedimensional dynamical systems, which allow for a fast, straightforward and accurate determination of linear multivariable models from measured inputoutput data.Abstract:
Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finitedimensional dynamical systems. These algorithms allow for a fast, straightforward
and accurate determination of linear multivariable models from measured inputoutput data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministicstochastic
subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms,processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of MATLAB® files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification
algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the MATLAB® files to ten practical problems. Since all necessary data and MATLAB® files are included, the reader can easily step through these applications, and thus get more insight in the algorithms.
Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization,mechatronics, chemical, electrical, mechanical and aeronautical engineering.read more
Citations
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Journal ArticleDOI
Proof of concept of the structural health monitoring of framed structures by a novel combined experimental and theoretical approach
TL;DR: In this paper, the authors proposed a combined experimental and numerical methodology to perform the vibration-based structural health monitoring (SHM) of structures of the civil engineering lying in seismic hazard zones.
Journal ArticleDOI
Recursive subspace identification with prior information using the constrained least squares approach
TL;DR: A recursive subspace identification algorithm incorporating prior information is developed using the constrained recursive least squares (CRLS) and it is shown via a simulation example that the state space model identified using the proposed algorithm possesses improved accuracy.
Journal ArticleDOI
Evaluation of optimal sensor placement algorithms for the Structural Health Monitoring of architectural heritage. Application to the Monastery of San Jerónimo de Buenavista (Seville, Spain)
Pablo Pachón,María Infantes,Margarita Cámara,Víctor Compán,Enrique García-Macías,Michael I. Friswell,Rafael Castro-Triguero +6 more
TL;DR: A design methodology of sensor networks based on OSP techniques suitable for historical structures is presented and evaluated with a case study of a Spanish secular building: the Monastery of San Jeronimo de Buenavista in Seville.
JournalDOI
Use of Time- and Frequency-Domain Approaches for Damage Detection in Civil Engineering Structures
TL;DR: In this article, the authors apply both time and frequency-domain-based approaches on real-life civil engineering structures and assess their capability for damage detection based on principal component analysis of the Hankel matrix built from output-only measurements and of Frequency Response Functions.
Journal ArticleDOI
An Extended Kalman Filter for Data-Enabled Predictive Control
TL;DR: In this paper, the authors propose to equip the recently introduced Data-enabled Predictive Control algorithm with a data-based Extended Kalman Filter to make use of additional available input-output data for reducing the effect of noise.
Related Papers (5)
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Reference-based stochastic subspace identification for output-only modal analysis
Bart Peeters,Guido De Roeck +1 more