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
Application of a subspace-based fault detection method to industrial structures
TL;DR: In this article, a statistical local approach based on covariance-driven stochastic subspace identification is proposed to monitor the health of a structure in operating conditions (e.g. rotating machinery, civil constructions subject to ambient excitations, etc.) and detect slight deviations in a modal model derived from in-operation measured data.
Journal ArticleDOI
Complex dynamics of a nonlinear aerospace structure: Experimental identification and modal interactions
TL;DR: In this article, the identification of a real-life aerospace structure possessing a strongly nonlinear component with multiple mechanical stops was carried out based upon experimental data, and the combined use of various analysis techniques, such as the wavelet transform and the restoring force surface method, brought different perspectives to the dynamics.
Journal ArticleDOI
A Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques
TL;DR: In this article, a predictive maintenance (PdM) system is proposed with the aim of predicting process behavior and scheduling control actions on the sensors in advance, and two different prediction techniques are employed and compared: the Kalman predictor and the particle filter with Gaussian kernel density estimator.
Journal ArticleDOI
Recursive Predictor-Based Subspace Identification With Application to the Real-Time Closed-Loop Tracking of Flutter
TL;DR: The recursive implementation of the novel recursive predictor-based subspace identification method is not only able to identify linear time-invariant models from measured data, but can also be used to track slowly time-varying dynamics if adaptive filters are used.
Design of Lightweight Footbridges for Human Induced Vibrations
Heinemeyer Cristoph,Butz Christiane,Keil Andreas,Schlaich Mike,Goldbeck Arndt,Trometor Stefan,Lukic Mladen,Chabrolin Bruno,Lemaire Armand,Martin Pierre-Olivier,Cunha Alvaro,Caetano Elsa +11 more
TL;DR: In this article, a guideline for footbridges design taking into account human induced vibrations is presented, and three worked examples of application of the proposed design methodology are exposed, namely, a simply supported beam and two existing footbridge.
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