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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.

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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

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

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Date deposited: 15 Aug 2016 10:32
Last modified: 09 Jan 2022 05:56

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Contributors

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

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