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
11 October 2016
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), .
(doi:10.1137/15M1031837).
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
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