Subspace-based multivariable system identification from frequency response data
TL;DR: Two noniterative subspace-based algorithms which identify linear, time-invariant MIMO (multi-input/multioutput) systems from frequency response data are presented.
Abstract: Two noniterative subspace-based algorithms which identify linear, time-invariant MIMO (multi-input/multioutput) systems from frequency response data are presented. The algorithms are related to the recent time-domain subspace identification techniques. The first algorithm uses equidistantly, in frequency, spaced data and is strongly consistent under weak noise assumptions. The second algorithm uses arbitrary frequency spacing and is strongly consistent under more restrictive noise assumptions, promising results are obtained when the algorithms are applied to real frequency data originating from a large flexible structure.
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01 Jan 1992
TL;DR: Two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data are presented: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set of equations.
Abstract: In this paper, we present two novel algorithms to realize a finite dimensional, linear time-invariant state-space model from input-output data. The algorithms have a number of common features. They are classified as one of the subspace model identification schemes, in that a major part of the identification problem consists of calculating specially structured subspaces of spaces defined by the input-output data. This structure is then exploited in the calculation of a realization. Another common feature is their algorithmic organization: an RQ factorization followed by a singular value decomposition and the solution of an overdetermined set (or sets) of equations. The schemes assume that the underlying system has an output-error structure and that a measurable input sequence is available. The latter characteristic indicates that both schemes are versions of the MIMO Output-Error State Space model identification (MOESP) approach. The first algorithm is denoted in particular as the (elementary MOESP scheme)...
660 citations
TL;DR: In this paper, the authors present a survey of the key developments which arose in the field since 2006, and illustrate state-of-the-art techniques using a real-world satellite structure.
353 citations
TL;DR: In this article, a flexure-based, piezoelectric stack-actuated XY nanopositioning stage was designed to combine the ability to scan over a relatively large range (25times25 mum) with high scanning speed.
Abstract: The design, identification, and control of a novel, flexure-based, piezoelectric stack-actuated XY nanopositioning stage are presented in this paper. The main goal of the design is to combine the ability to scan over a relatively large range (25times25 mum) with high scanning speed. Consequently, the stage is designed to have its first dominant mode at 2.7 kHz. Cross-coupling between the two axes is kept to -35 dB, low enough to utilize single-input--single-output control strategies for tracking. Finite-element analysis (FEA) is used during the design process to analyze the mechanical resonance frequencies, travel range, and cross-coupling between the X- and Y-axes of the stage. Nonlinearities such as hysteresis are present in such stages. These effects, which exist due to the use of piezoelectric stacks for actuation, are minimized using charge actuation. The integral resonant control method is applied in conjunction with feedforward inversion technique to achieve high-speed and accurate scanning performances, up to 400 Hz.
347 citations
TL;DR: A hierarchical gradient iterative algorithm and a hierarchical stochastic gradient algorithm are proposed and it is proved that the parameter estimation errors given by the algorithms converge to zero for any initial values under persistent excitation.
313 citations
TL;DR: A method to model nonlinear systems using polynomial nonlinear state space equations by identifying first the best linear approximation of the system under test is proposed.
247 citations
Cites methods from "Subspace-based multivariable system..."
...Frequency Domain Subspace Identification Algorithm [44] 1....
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...For this, we make use of the frequency domain subspace algorithm in [44] which allows to incorporate covariance information for non uniformly spaced frequency domain data....
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References
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TL;DR: Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise.
Abstract: An approach to the general problem of signal parameter estimation is described. The algorithm differs from its predecessor in that a total least-squares rather than a standard least-squares criterion is used. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC. >
6,273 citations
TL;DR: In this paper, it is shown that principal component analysis (PCA) is a powerful tool for coping with structural instability in dynamic systems, and it is proposed that the first step in model reduction is to apply the mechanics of minimal realization using these working subspaces.
Abstract: Kalman's minimal realization theory involves geometric objects (controllable, unobservable subspaces) which are subject to structural instability. Specifically, arbitrarily small perturbations in a model may cause a change in the dimensions of the associated subspaces. This situation is manifested in computational difficulties which arise in attempts to apply textbook algorithms for computing a minimal realization. Structural instability associated with geometric theories is not unique to control; it arises in the theory of linear equations as well. In this setting, the computational problems have been studied for decades and excellent tools have been developed for coping with the situation. One of the main goals of this paper is to call attention to principal component analysis (Hotelling, 1933), and an algorithm (Golub and Reinsch, 1970) for computing the singular value decompositon of a matrix. Together they form a powerful tool for coping with structural instability in dynamic systems. As developed in this paper, principal component analysis is a technique for analyzing signals. (Singular value decomposition provides the computational machinery.) For this reason, Kalman's minimal realization theory is recast in terms of responses to injected signals. Application of the signal analysis to controllability and observability leads to a coordinate system in which the "internally balanced" model has special properties. For asymptotically stable systems, this yields working approximations of X_{c}, X_{\bar{o}} , the controllable and unobservable subspaces. It is proposed that a natural first step in model reduction is to apply the mechanics of minimal realization using these working subspaces.
5,134 citations
"Subspace-based multivariable system..." refers background in this paper
...For the case when the true systems are infinite-dimensional, nice extensions have been reported in [36]....
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Book•
01 May 1981
TL;DR: This book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical.
Abstract: This book will be most useful to applied mathematicians, communication engineers, signal processors, statisticians, and time series researchers, both applied and theoretical. Readers should have some background in complex function theory and matrix algebra and should have successfully completed the equivalent of an upper division course in statistics.
3,231 citations
TL;DR: In this paper, a complete characterization of all rational functions that minimize the Hankel-norm is derived, and the solution to the latter problem is via results on balanced realizations, all-pass functions and the inertia of matrices, all in terms of the solutions to Lyapunov equations.
Abstract: The problem of approximating a multivariable transfer function G(s) of McMillan degree n, by Ĝ(s) of McMillan degree k is considered. A complete characterization of all approximations that minimize the Hankel-norm is derived. The solution involves a characterization of all rational functions Ĝ(s) + F(s) that minimize where Ĝ(s) has McMillan degree k, and F(s) is anticavisal. The solution to the latter problem is via results on balanced realizations, all-pass functions and the inertia of matrices, all in terms of the solutions to Lyapunov equations. It is then shown that where σ k+1(G(s)) is the (k+l)st Hankel singular value of G(s) and for one class of optimal Hankel-norm approximations. The method is not computationally demanding and is applied to a 12-state model.
2,980 citations
Book•
01 Jan 2001
TL;DR: This edition of A Course in Probability Theory includes an introduction to measure theory that expands the market, as this treatment is more consistent with current courses.
Abstract: Since the publication of the first edition of this classic textbook over thirty years ago, tens of thousands of students have used A Course in Probability Theory. New in this edition is an introduction to measure theory that expands the market, as this treatment is more consistent with current courses. While there are several books on probability, Chung's book is considered a classic, original work in probability theory due to its elite level of sophistication.
2,647 citations