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
System identification-based control of an unmanned autonomous wind-propelled catamaran
TL;DR: In this paper, an autonomous catamaran, based on a modified Prindle-19 day-sailing boat and fitted with several sensors and actuators was built to test the viability of GPS-based system identification for precision control.
Book
Investigation of damage detection methodologies for structural health monitoring
TL;DR: In this article, different damage detection methods are investigated for global condition assessment of structures for structural health monitoring (SHM) systems, including parametric features such as modal flexibility, modal curvature and un-scaled flexibility.
Proceedings ArticleDOI
Dynamic tracking of non-stationarity in human ECoG activity
TL;DR: It is demonstrated thatNon-stationary dynamics exist in high-dimensional human ECoG signals over long time-periods, and that the proposed adaptive SSM identification algorithm can successfully track these non-stationarities.
Journal ArticleDOI
Data-Driven Unknown-Input Observers and State Estimation
TL;DR: In this paper, the authors provide necessary and sufficient conditions on the data collected from the system for the existence of a UIO providing asymptotically converging state estimates, and propose a purely data-driven algorithm for their computation.
Spectral approaches to learning predictive representations
Geoffrey J. Gordon,Byron Boots +1 more
TL;DR: In this paper, spectral subspace identification (SSA) is proposed to learn compact, accurate, predictive models of partially observable dynamical systems directly from sequences of action-observation pairs.
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