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Identification and data-driven reduced-order modeling for linear conservative port- and self-adjoint Hamiltonian systems

Identification and data-driven reduced-order modeling for linear conservative port- and self-adjoint Hamiltonian systems
Identification and data-driven reduced-order modeling for linear conservative port- and self-adjoint Hamiltonian systems
Given a sufficiently numerous set of vector exponential trajectories of a conservative port-Hamiltonian system and the supply rate, we compute a corresponding set of state trajectories by factorizing a constant Pick-like matrix. State equations are then obtained by solving a system of linear equations involving the system trajectories and the computed state ones. If a factorization of only a principal submatrix of the Pick matrix is performed, our procedure yields a lower-order conservative port-Hamiltonian model obtained by projection of the full-order one. We also describe a similar approach to identification and model-order reduction for self-adjoint Hamiltonian systems
145-150
Rapisarda, Paolo
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
van der Schaft, Arjan
d7e3477b-ce6d-4443-98a0-c65360437f03
Rapisarda, Paolo
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
van der Schaft, Arjan
d7e3477b-ce6d-4443-98a0-c65360437f03

Rapisarda, Paolo and van der Schaft, Arjan (2013) Identification and data-driven reduced-order modeling for linear conservative port- and self-adjoint Hamiltonian systems. 52nd IEEE Conference on Decision and Control, , Florence, Italy. 10 - 13 Dec 2013. pp. 145-150 .

Record type: Conference or Workshop Item (Paper)

Abstract

Given a sufficiently numerous set of vector exponential trajectories of a conservative port-Hamiltonian system and the supply rate, we compute a corresponding set of state trajectories by factorizing a constant Pick-like matrix. State equations are then obtained by solving a system of linear equations involving the system trajectories and the computed state ones. If a factorization of only a principal submatrix of the Pick matrix is performed, our procedure yields a lower-order conservative port-Hamiltonian model obtained by projection of the full-order one. We also describe a similar approach to identification and model-order reduction for self-adjoint Hamiltonian systems

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Published date: 2013
Venue - Dates: 52nd IEEE Conference on Decision and Control, , Florence, Italy, 2013-12-10 - 2013-12-13
Organisations: Southampton Wireless Group

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Local EPrints ID: 360955
URI: http://eprints.soton.ac.uk/id/eprint/360955
PURE UUID: 9f40d953-19bb-42cc-910c-07ec72b58f36

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Date deposited: 07 Jan 2014 16:10
Last modified: 14 Mar 2024 15:44

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Contributors

Author: Paolo Rapisarda
Author: Arjan van der Schaft

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