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
Brief paper: Kalman filters in non-uniformly sampled multirate systems: For FDI and beyond
TL;DR: A Kalman filter-based methodology for unified detection and isolation of sensor, actuator, and process faults in the NUSM system with analysis on fault detectability and isolability is investigated.
Journal ArticleDOI
Linear parameter varying battery model identification using subspace methods
Y. Hu,Stephen Yurkovich +1 more
TL;DR: In this article, a comprehensive identification algorithm that uses linear-algebra-based subspace methods to identify a parameter varying state variable model that can describe the input-to-output dynamics of a battery under various operating conditions is presented.
Journal ArticleDOI
The effect of time synchronization of wireless sensors on the modal analysis of structures
TL;DR: A theoretical framework for analysis of the impact created by time delays in the measured system response on the reconstruction of mode shapes using the popular frequency domain decomposition (FDD) technique is presented.
Journal ArticleDOI
Substructure system identification from coupled system test data
Per Sjövall,Thomas Abrahamsson +1 more
TL;DR: It is shown that the load identification and thus also the subsystem identification is sensitive to the existence of general anti-resonances in the frequency domain of interest and the methods for validation of an identified subsystem are discussed.
Journal ArticleDOI
Estimation of CNC machine–tool dynamic parameters based on random cutting excitation through operational modal analysis
TL;DR: In this article, a novel random excitation technique based on cutting is presented to meet the white noise excitation requirement. But this technique is realized by interrupted cutting of a narrow workpiece step while spindle rotating randomly.
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