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
Controller performance analysis with LQG benchmark obtained under closed loop conditions.
Ramesh Kadali,Biao Huang +1 more
TL;DR: A data driven subspace approach to calculate the LQG benchmark under closed-loop conditions with certain external excitations is proposed and the optimal L QG-benchmark variances are obtained directly from the subspace matrices corresponding to the deterministic inputs and the stochastic inputs, which are identified using closed- loop data with setpoint excitation.
Journal ArticleDOI
Modelling and prediction of the dynamic responses of large-scale brain networks during direct electrical stimulation
Yuxiao Yang,Shaoyu Qiao,Omid G. Sani,J. Isaac Sedillo,Breonna Ferrentino,Bijan Pesaran,Bijan Pesaran,Maryam M. Shanechi +7 more
TL;DR: In this article, the authors developed dynamic input-output models that predict multiregional dynamics of brain networks in response to temporally varying patterns of ongoing micro-stimulation.
Journal ArticleDOI
Global Sensitivity-Based Model Updating for Heritage Structures
TL;DR: The concept of global SA is applied for the first time to complex monumental structures, and a comparative view is offered on more classical local SA approaches.
Posted Content
Modern Koopman Theory for Dynamical Systems.
TL;DR: Koopman spectral theory has emerged as a dominant perspective over the past decade, in which nonlinear dynamics are represented in terms of an infinite-dimensional linear operator acting on the space of all possible measurement functions of the system as discussed by the authors.
Journal ArticleDOI
Experimental frequencies and damping ratios for historical masonry arch bridges
TL;DR: In this article, the authors determine the frequency and damping ratios of historical masonry arch bridges experimentally using the Operational Modal Analysis Method (OMA) and Stochastic Subspace Identification (SSI).
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