Decomposition and Distributed Control of Integrated Lumped and Distributed Parameter Process Networks

Manjiri Moharir; Davood B. Pourkargar; Ali Almansoori; Prodromos Daoutidis

doi:10.1109/CDC.2018.8619216

Decomposition and Distributed Control of Integrated Lumped and Distributed Parameter Process Networks

Manjiri Moharir, Davood B. Pourkargar, Ali Almansoori, Prodromos Daoutidis

Chemical and Petroleum Engineering

University of Minnesota Twin Cities

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This work considers the problem of achieving model-based plant-wide control of a prototypical process network comprising interconnected lumped and distributed parameter systems. A community detection algorithm is used to obtain an optimal decomposition of this network for distributed control. The community detection is performed on a novel graph representation of the dynamics of the network, which accounts systematically for the interconnections among the variables of the process systems, including the different types of variables of the distributed parameter systems. The resulting distributed model predictive control implementation is shown to be computationally tractable, with performance close to that of centralized model predictive control.

Original language	British English
Title of host publication	2018 IEEE Conference on Decision and Control, CDC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2908-2913
Number of pages	6
ISBN (Electronic)	9781538613955
DOIs	https://doi.org/10.1109/CDC.2018.8619216
State	Published - 2 Jul 2018
Event	57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States Duration: 17 Dec 2018 → 19 Dec 2018

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2018-December
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	57th IEEE Conference on Decision and Control, CDC 2018
Country/Territory	United States
City	Miami
Period	17/12/18 → 19/12/18

Access to Document

10.1109/CDC.2018.8619216

Cite this

Moharir, M., Pourkargar, D. B., Almansoori, A., & Daoutidis, P. (2018). Decomposition and Distributed Control of Integrated Lumped and Distributed Parameter Process Networks. In 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 2908-2913). Article 8619216 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8619216

@inproceedings{c2314cdb131f411bade70a0c09471477,

title = "Decomposition and Distributed Control of Integrated Lumped and Distributed Parameter Process Networks",

abstract = "This work considers the problem of achieving model-based plant-wide control of a prototypical process network comprising interconnected lumped and distributed parameter systems. A community detection algorithm is used to obtain an optimal decomposition of this network for distributed control. The community detection is performed on a novel graph representation of the dynamics of the network, which accounts systematically for the interconnections among the variables of the process systems, including the different types of variables of the distributed parameter systems. The resulting distributed model predictive control implementation is shown to be computationally tractable, with performance close to that of centralized model predictive control.",

author = "Manjiri Moharir and Pourkargar, {Davood B.} and Ali Almansoori and Prodromos Daoutidis",

note = "Funding Information: Partial financial support from the Khalifa University of Science and Technology is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2018 IEEE.; 57th IEEE Conference on Decision and Control, CDC 2018 ; Conference date: 17-12-2018 Through 19-12-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/CDC.2018.8619216",

language = "British English",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2908--2913",

booktitle = "2018 IEEE Conference on Decision and Control, CDC 2018",

address = "United States",

}

Moharir, M, Pourkargar, DB, Almansoori, A & Daoutidis, P 2018, Decomposition and Distributed Control of Integrated Lumped and Distributed Parameter Process Networks. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8619216, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 2908-2913, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 17/12/18. https://doi.org/10.1109/CDC.2018.8619216

Decomposition and Distributed Control of Integrated Lumped and Distributed Parameter Process Networks. / Moharir, Manjiri; Pourkargar, Davood B.; Almansoori, Ali et al.
2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2908-2913 8619216 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Decomposition and Distributed Control of Integrated Lumped and Distributed Parameter Process Networks

AU - Moharir, Manjiri

AU - Pourkargar, Davood B.

AU - Almansoori, Ali

AU - Daoutidis, Prodromos

PY - 2018/7/2

Y1 - 2018/7/2

N2 - This work considers the problem of achieving model-based plant-wide control of a prototypical process network comprising interconnected lumped and distributed parameter systems. A community detection algorithm is used to obtain an optimal decomposition of this network for distributed control. The community detection is performed on a novel graph representation of the dynamics of the network, which accounts systematically for the interconnections among the variables of the process systems, including the different types of variables of the distributed parameter systems. The resulting distributed model predictive control implementation is shown to be computationally tractable, with performance close to that of centralized model predictive control.

AB - This work considers the problem of achieving model-based plant-wide control of a prototypical process network comprising interconnected lumped and distributed parameter systems. A community detection algorithm is used to obtain an optimal decomposition of this network for distributed control. The community detection is performed on a novel graph representation of the dynamics of the network, which accounts systematically for the interconnections among the variables of the process systems, including the different types of variables of the distributed parameter systems. The resulting distributed model predictive control implementation is shown to be computationally tractable, with performance close to that of centralized model predictive control.

UR - http://www.scopus.com/inward/record.url?scp=85062185632&partnerID=8YFLogxK

U2 - 10.1109/CDC.2018.8619216

DO - 10.1109/CDC.2018.8619216

M3 - Conference contribution

AN - SCOPUS:85062185632

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 2908

EP - 2913

BT - 2018 IEEE Conference on Decision and Control, CDC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 57th IEEE Conference on Decision and Control, CDC 2018

Y2 - 17 December 2018 through 19 December 2018

ER -

Moharir M, Pourkargar DB, Almansoori A, Daoutidis P. Decomposition and Distributed Control of Integrated Lumped and Distributed Parameter Process Networks. In 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2908-2913. 8619216. (Proceedings of the IEEE Conference on Decision and Control). doi: 10.1109/CDC.2018.8619216

Decomposition and Distributed Control of Integrated Lumped and Distributed Parameter Process Networks

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this