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
Merging sensor data from multiple measurement set-ups for non-stationary subspace-based modal analysis
TL;DR: The purpose of this paper is to investigate as to how subspace-based output-only modal analysis algorithms can be adapted to handle the multi-patch measurements set-up and to show the relevance and usefulness of the proposed algorithm.
Journal ArticleDOI
Frequency domain subspace-based identification of discrete-time power spectra from nonuniformly spaced measurements
Hüseyin Akçay,Semiha Turkay +1 more
TL;DR: A new subspace-based algorithm for the identification of multi-input/multi-output, square, discrete-time, linear-time invariant systems from nonuniformly spaced power spectrum measurements is presented.
Journal ArticleDOI
Rapidly Exploring Random Cycles: Persistent Estimation of Spatiotemporal Fields With Multiple Sensing Robots
Xiaodong Lan,Mac Schwager +1 more
TL;DR: Two new sampling-based path-planning algorithms are proposed to find periodic trajectories for the sensing robots that minimize the largest eigenvalue of the error covariance matrix over an infinite horizon.
Journal ArticleDOI
A frequency and spatial domain decomposition method for operational strain modal analysis and its application
TL;DR: In this article, a frequency and spatial domain decomposition method for operational modal analysis making use of strain measurements is presented, which can be applied to various engineering problems more commonly due to its advantages in real life implementations.
Journal ArticleDOI
Structural health monitoring and seismic response assessment of bridge structures using target-tracking digital image correlation
TL;DR: Practical DIC sampling rates were used to accurately monitor and capture the dynamic response of bridges, which shows a high potential for using DIC for larger structural health monitoring applications and future reconnaissance works.
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