The University of Southampton
University of Southampton Institutional Repository

State-space modelling of two-dimensional vector-exponential trajectories

State-space modelling of two-dimensional vector-exponential trajectories
State-space modelling of two-dimensional vector-exponential trajectories
We solve two problems in modelling polynomial vector-exponential trajectories dependent on two independent variables. In the first one we assume that the data-generating system has no inputs, and we compute a state representation of the most powerful unfalsified Model for this data. In the second instance we assume that the data-generating system is controllable and quarter-plane causal, and we compute a Roesser input-state-output model. We provide procedures for solving these identification problems, both based on the factorization of constant matrices directly constructed from the data, from which state trajectories can be computed.
2734-2753
Rapisarda, P.
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
Antoulas, A. C.
0a38bcd3-29f6-431c-86d9-9de8856212dd
Rapisarda, P.
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
Antoulas, A. C.
0a38bcd3-29f6-431c-86d9-9de8856212dd

Rapisarda, P. and Antoulas, A. C. (2016) State-space modelling of two-dimensional vector-exponential trajectories. SIAM Journal on Control and Optimization, 54 (5), 2734-2753. (doi:10.1137/15M1031837).

Record type: Article

Abstract

We solve two problems in modelling polynomial vector-exponential trajectories dependent on two independent variables. In the first one we assume that the data-generating system has no inputs, and we compute a state representation of the most powerful unfalsified Model for this data. In the second instance we assume that the data-generating system is controllable and quarter-plane causal, and we compute a Roesser input-state-output model. We provide procedures for solving these identification problems, both based on the factorization of constant matrices directly constructed from the data, from which state trajectories can be computed.

Text
2DMPUMVers8(AfterReviews).pdf - Accepted Manuscript
Download (319kB)

More information

Accepted/In Press date: 10 August 2016
e-pub ahead of print date: 11 October 2016
Published date: 11 October 2016
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 399367
URI: http://eprints.soton.ac.uk/id/eprint/399367
PURE UUID: 866facae-1b33-4775-9a4b-8bea6fcbf693

Catalogue record

Date deposited: 15 Aug 2016 10:32
Last modified: 15 Mar 2024 05:48

Export record

Altmetrics

Contributors

Author: P. Rapisarda
Author: A. C. Antoulas

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×