Subspace Identification for Linear Systems Theory — Implementation — Applications

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 clas...

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Bibliographic Details
Main Authors: van Overschee, Peter, de Moor, B.L. (Author)
Format: eBook
Language:English
Published: New York, NY Springer US 1996, 1996
Edition:1st ed. 1996
Subjects:
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
Table of Contents:
  • 1 Introduction, Motivation and Geometric Tools
  • 1.1 Models of systems and system identification
  • 1.2 A new generation of system identification algorithms
  • 1.3 Overview
  • 1.4 Geometric tools
  • 1.5 Conclusions
  • 2 Deterministic Identification
  • 2.1 Deterministic systems
  • 2.2 Geometric properties of deterministic systems
  • 2.3 Relation to other algorithms
  • 2.4 Computing the system matrices
  • 2.5 Conclusions
  • 3 Stochastic Identification
  • 3.1 Stochastic systems
  • 3.2 Geometric properties of stochastic systems
  • 3.3 Relation to other algorithms
  • 3.4 Computing the system matrices
  • 3.5 Conclusions
  • 4 Combined Deterministic-Stochastic Identification
  • 4.1 Combined systems
  • 4.2 Geometric properties of combined systems
  • 4.3 Relation to other algorithms
  • 4.4 Computing the system matrices
  • 4.5 Connections to the previous Chapters
  • 4.6 Conclusions
  • 5 State Space Bases and Model Reduction
  • 5.1 Introduction
  • 5.2 Notation
  • 5.3 Frequency weighted balancing
  • 5.4 Subspace identification and frequency weighted balancing
  • 5.5 Consequences for reduced order identification
  • 5.6 Example
  • 5.7 Conclusions
  • 6 Implementation and Applications
  • 6.1 Numerical Implementation
  • 6.2 Interactive System Identification
  • 6.3 An Application of ISID
  • 6.4 Practical examples in Matlab
  • 6.5 Conclusions
  • 7 Conclusions and Open Problems
  • 7.1 Conclusions
  • 7.2 Open problems
  • A Proofs
  • A.1 Proof of formula (2.16)
  • A.2 Proof of Theorem 6
  • A.3 Note on the special form of the Kalman filter
  • A.4 Proof of Theorem 8
  • A.5 Proof of Theorem 9
  • A.6 Proof of Theorem 11
  • A.7 Proof of Theorem 12
  • A.8 Proof of Lemma 2
  • A.9 Proof of Theorem 13
  • A.10 Proof of Corollary 2 and 3
  • A.11 Proof of Theorem 14
  • B Matlab Functions
  • B.1 Getting started
  • B.2 Matlab Reference
  • B.2.1 Directory: ‘subfun’
  • B.2.2 Directory: ‘applic’
  • B.2.3 Directory: ‘examples’
  • B.2.4 Directory: ‘figures’
  • C Notation
  • References