Nuclear Norm Subspace Identification Of Continuous Time State-Space Models

Varanasi, Santhosh Kumar and Jampana, Phanindra Varma (2018) Nuclear Norm Subspace Identification Of Continuous Time State-Space Models. IFAC-PapersOnLine, 51 (1). pp. 530-535. ISSN 2405-8963

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Abstract

Subspace identification techniques derive approximate models rather than models that are optimal with respect to a goodness of fit criterion. To obtain low rank models, a nuclear norm minimization method for estimating the system matrices of linear time invariant continuous time state-space models in the presence of measurement noise is proposed. In the proposed approach, Generalized Poisson Moment Functional (GPMF) method is used to circumvent the time-derivative problem which is inherent in continuous time models. To make the proposed algorithm consistent, instrumental variables (Hankel matrix of past inputs) are considered. The accuracy of the proposed method is demonstrated with the help of numerical simulations on a variety of systems.

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IITH Creators:

IITH Creators	ORCiD
Jampana, Phanindra Varma	http://orcid.org/0000-0002-9678-5249

Item Type:

Article

Uncontrolled Keywords:

System Identification, Nuclear Norm Minimization, Continuous Time, State-Space Models, Generalized Poisson Moment Functionals, Semi-Definite Programming

Subjects:

Chemical Engineering

Divisions:

Department of Chemical Engineering

Depositing User:

Team Library

Date Deposited:

27 Jun 2018 04:58

Last Modified:

27 Jun 2018 04:58

URI:

http://raiithold.iith.ac.in/id/eprint/4080