CCE Faculty Articles
Subspace Identification 2-D CRSD Roesser Models with Deterministic-Stochastic Inputs: A State Computation Approach
Document Type
Article
Publication Title
IEEE Transactions on Control Systems Technology
ISSN
1063-6536
Publication Date
5-2017
Abstract
In this brief, we present a subspace system identification framework for 2-D separable-in-denominator systems with deterministic-stochastic inputs in the Roesser form. The advantage of the proposed framework is that it is based on the computation of state matrices, as opposed to current algorithms that compute the system parameter matrices from Markov parameters and the observability matrix. As such, it does not require solving specialized Toeplitz or Hankel systems of equations while computing the system parameter matrices. In addition, the problem is broken down into two simple oblique projection computations—one in the horizontal direction and one in the vertical direction. Within this framework, Numerical algorithms for Subspace State Space System IDentification (N4SID), Past-Output Multivariable Output-Error State-sPace (PO-MOESP), and Canonical Variate Analysis (CVA) type algorithms are obtained. Simulation results show that the algorithms are accurate and provide new alternatives for modeling and identifying 2-D causal, recursive, and separablein-denominator Roesser models.
DOI
10.1109/TCST.2016.2581149
Volume
25
Issue
3
First Page
1108
Last Page
1115
NSUWorks Citation
Ramos, Jose A.; Alenany, Ahmed; and Mercere, Guillaume, "Subspace Identification 2-D CRSD Roesser Models with Deterministic-Stochastic Inputs: A State Computation Approach" (2017). CCE Faculty Articles. 439.
https://nsuworks.nova.edu/gscis_facarticles/439
