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
N4SID and MOESP Algorithms to Highlight the Ill-conditioning into Subspace Identification
TL;DR: The obtained results proved that the algorithm based on orthogonal projection MOESP, overcomes the situation of ill-conditioning in the Hankel’s block, and thereby improving the estimation of parameters.
Journal ArticleDOI
Estimation of the electromechanical characteristics of power systems based on a revised stochastic subspace method and the stabilization diagram
Jingmin Ni,Chen Shen,Feng Liu +2 more
TL;DR: A revised stochastic subspace method is proposed by introducing reference channels, which can estimate the modes and the mode shapes simultaneously with great computational efficiency and accuracy, and has the potential of being used on-line.
Dissertation
Global and multi-input-multi-output (mimo) extensions of the algorithm of mode isolation (ami)
TL;DR: This dissertation presents a hybrid multi-input-multi-output (MIMO) implementation of the algorithm of mode isolation that improves the performance of AMI for systems with very close or weakly excited modes.
Scaling the Mode Shapes of a Building Model by Mass Changes
TL;DR: In this paper, the authors used the mass change technique to estimate the scaling factor of a 4-storey building with a 1.4-scale model and showed that the uncertainty of the estimated scaling factor can be controlled by using a mass change matrix with respect to the initial mass matrix.
Journal ArticleDOI
Nonlinear discrete-time models: state-space vs. I/O representations
R.K. Pearson,Ü. Kotta +1 more
TL;DR: In this article, the authors compare state-space and input-output realizations for nonlinear discrete-time dynamic models and show that for linear models, these two realizations are essentially equivalent and their structures are closely related.
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