Nuclear norm subspace identification of continuous time state–space models with missing outputs

Varanasi, Santhosh Kumar and Jampana, Phanindra Varma (2020) Nuclear norm subspace identification of continuous time state–space models with missing outputs. Control Engineering Practice, 95. ISSN 09670661

Full text not available from this repository. (Request a copy)


Subspace identification methods using Generalized Poisson Moment Functionals (GPMF) have been proposed previously to tackle the problem of derivative estimation in continuous time (CT) systems. In this paper, a convergence result underpinning the GPMF methods for continuous time identification is detailed. Based on this, a CT-MOESP method is proposed to estimate the system matrices in state–space models. Since these results hold in the asymptotic case where the number of data points tend to infinity, Nuclear Norm Minimization (NNM) is used to integrate the low rank approximation step in subspace identification with a goodness of fit criterion. This paper extends these existing discrete time methods to continuous time by formulating the NNM optimization into the framework of the Alternating Direction Method of Multipliers (ADMM) algorithm. On the numerical front, the accuracy of the proposed method is demonstrated with the help of simulations on two systems frequently cited in literature. An industrial dryer application is considered in order to demonstrate the practical applicability of proposed method.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Jampana, Phanindra Varma
Item Type: Article
Uncontrolled Keywords: Alternating direction method of multipliers, Continuous time, Generalized Poisson moment functionals, Nuclear norm minimization, State–space models, System identification, Indexed in Scopus
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: Team Library
Date Deposited: 04 Dec 2019 04:28
Last Modified: 04 Dec 2019 04:28
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 7104 Statistics for this ePrint Item