Subspace-based identification for linear and nonlinear systems

This paper deals with the basic subspace algorithm for time-invariant systems. A simplified proof of the fact that the state sequence and/or the observability matrix of the dynamical system can be determined directly from input-output data is provided. Some existing identification algorithms for lin...

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Published in2005 American Control Conference pp. 2320 - 2334 vol. 4
Main Authors Palanthandalam-Madapusi, H.J., Lacy, S., Hoagg, J.B., Bernstein, D.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Analytical models
Control system synthesis
Mathematical model
Nonlinear dynamical systems
Nonlinear systems
Predictive models
State estimation
System analysis and design
System identification
Time domain analysis
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ISBN0780390989
9780780390980
9780780390997
0780390997
ISSN0743-1619
DOI10.1109/ACC.2005.1470314

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Abstract This paper deals with the basic subspace algorithm for time-invariant systems. A simplified proof of the fact that the state sequence and/or the observability matrix of the dynamical system can be determined directly from input-output data is provided. Some existing identification algorithms for linear time-varying systems are presented. The paper also covers the bulk of the existing subspace-based nonlinear identification algorithms including Hammerstein and nonlinear feedback identification, Hammerstein-Wiener identification for Wiener systems, linear parameter-varying system identification, and bilinear system identification.
AbstractList This paper deals with the basic subspace algorithm for time-invariant systems. A simplified proof of the fact that the state sequence and/or the observability matrix of the dynamical system can be determined directly from input-output data is provided. Some existing identification algorithms for linear time-varying systems are presented. The paper also covers the bulk of the existing subspace-based nonlinear identification algorithms including Hammerstein and nonlinear feedback identification, Hammerstein-Wiener identification for Wiener systems, linear parameter-varying system identification, and bilinear system identification.
Mathematical models describe the dynamic behavior of a system as a function of time, and arise in all scientific disciplines. These mathematical models are used for simulation, operator training, analysis, monitoring, fault detection, prediction, optimization, control system designs and quality control. System identification is the process of constructing mathematical models of dynamical systems using measured data. The identified models can be used for output prediction, system analysis and diagnostics, system design, and control. Identification of linear models has been well studied and numerous frequency-domain and time-domain methods are available [21]. Among the available time-domain methods, subspace algorithms are an important class of algorithms for identifying linear state-space models.
Author Bernstein, D.S.
Lacy, S.
Hoagg, J.B.
Palanthandalam-Madapusi, H.J.
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Snippet This paper deals with the basic subspace algorithm for time-invariant systems. A simplified proof of the fact that the state sequence and/or the observability...
Mathematical models describe the dynamic behavior of a system as a function of time, and arise in all scientific disciplines. These mathematical models are...
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SubjectTerms Analytical models
Control system synthesis
Mathematical model
Nonlinear dynamical systems
Nonlinear systems
Predictive models
State estimation
System analysis and design
System identification
Time domain analysis
Title Subspace-based identification for linear and nonlinear systems
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