Structure-based clustering algorithm for model reduction of large-scale network systems

Muhammad Umar B. Niazi; Xiaodong Cheng; Carlos Canudas-De-Wit; Jacquelien M.A. Scherpen

doi:10.1109/CDC40024.2019.9029349

Structure-based clustering algorithm for model reduction of large-scale network systems

Muhammad Umar B. Niazi
, Xiaodong Cheng
, Carlos Canudas-De-Wit
, Jacquelien M.A. Scherpen

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

6 Citaten (Scopus)

Samenvatting

A model reduction technique is presented that identifies and aggregates clusters in a large-scale network system and yields a reduced model with tractable dimension. The network clustering problem is translated to a graph reduction problem, which is formulated as a minimization of distance from lumpability. The problem is a non-convex, mixed-integer optimization problem and only depends on the graph structure of the system. We provide a heuristic algorithm to identify clusters that are not only suboptimal but are also connected, that is, each cluster forms a connected induced subgraph in the network system.

Originele taal-2	Engels
Titel	2019 IEEE 58th Conference on Decision and Control, CDC 2019
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	5038-5043
Aantal pagina's	6
ISBN van elektronische versie	9781728113982
DOI's	https://doi.org/10.1109/CDC40024.2019.9029349
Status	Gepubliceerd - 12 mrt. 2020
Evenement	2019 IEEE 58th Conference on Decision and Control (CDC) - Nice, France, Nice, Frankrijk Duur: 11 dec. 2019 → 13 dec. 2019 Congresnummer: 58 https://cdc2019.ieeecss.org/

Congres

Congres	2019 IEEE 58th Conference on Decision and Control (CDC)
Verkorte titel	CDC 2019
Land/Regio	Frankrijk
Stad	Nice
Periode	11/12/19 → 13/12/19
Internet adres	https://cdc2019.ieeecss.org/

Financiering

† M.U.B. Niazi and C. Canudas-de-Wit are with GIPSA-Lab, CNRS, Grenoble, France. Email: {muhammad-umar-b.niazi,carlos.canudas-de-wit}@ gipsa-lab.fr § X. Cheng is with Control Systems Group, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands. Email: [email protected] ‡ J.M.A. Scherpen is with Jan C. Willems Center for Systems and Control, Engineering and Technology Institute, Faculty of Science and Engineering, University of Groningen, Groningen, Netherlands. Email: [email protected] This work is supported by European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme, ERC-AdG no. 694209, Scale-FreeBack (Website: http://scale-freeback.eu/). This work is supported by European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme, ERC-AdG no. 694209, Scale-FreeBack (Website: http://scale-freeback.eu/).

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10.1109/CDC40024.2019.9029349

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title = "Structure-based clustering algorithm for model reduction of large-scale network systems",

abstract = "A model reduction technique is presented that identifies and aggregates clusters in a large-scale network system and yields a reduced model with tractable dimension. The network clustering problem is translated to a graph reduction problem, which is formulated as a minimization of distance from lumpability. The problem is a non-convex, mixed-integer optimization problem and only depends on the graph structure of the system. We provide a heuristic algorithm to identify clusters that are not only suboptimal but are also connected, that is, each cluster forms a connected induced subgraph in the network system.",

keywords = "clustering algorithm, Large-scale systems, lumpa-bility, model reduction",

author = "Niazi, \{Muhammad Umar B.\} and Xiaodong Cheng and Carlos Canudas-De-Wit and Scherpen, \{Jacquelien M.A.\}",

year = "2020",

month = mar,

day = "12",

doi = "10.1109/CDC40024.2019.9029349",

language = "English",

pages = "5038--5043",

booktitle = "2019 IEEE 58th Conference on Decision and Control, CDC 2019",

publisher = "Institute of Electrical and Electronics Engineers",

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note = "58th IEEE Conference on Decision and Control, CDC 2019, CDC 2019 ; Conference date: 11-12-2019 Through 13-12-2019",

url = "https://cdc2019.ieeecss.org/",

}

Niazi, MUB, Cheng, X, Canudas-De-Wit, C & Scherpen, JMA 2020, Structure-based clustering algorithm for model reduction of large-scale network systems. in 2019 IEEE 58th Conference on Decision and Control, CDC 2019., 9029349, Institute of Electrical and Electronics Engineers, blz. 5038-5043, 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, Frankrijk, 11/12/19. https://doi.org/10.1109/CDC40024.2019.9029349

Structure-based clustering algorithm for model reduction of large-scale network systems. / Niazi, Muhammad Umar B.; Cheng, Xiaodong; Canudas-De-Wit, Carlos et al.
2019 IEEE 58th Conference on Decision and Control, CDC 2019. Institute of Electrical and Electronics Engineers, 2020. blz. 5038-5043 9029349.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

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AU - Canudas-De-Wit, Carlos

AU - Scherpen, Jacquelien M.A.

N1 - Conference code: 58

PY - 2020/3/12

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N2 - A model reduction technique is presented that identifies and aggregates clusters in a large-scale network system and yields a reduced model with tractable dimension. The network clustering problem is translated to a graph reduction problem, which is formulated as a minimization of distance from lumpability. The problem is a non-convex, mixed-integer optimization problem and only depends on the graph structure of the system. We provide a heuristic algorithm to identify clusters that are not only suboptimal but are also connected, that is, each cluster forms a connected induced subgraph in the network system.

AB - A model reduction technique is presented that identifies and aggregates clusters in a large-scale network system and yields a reduced model with tractable dimension. The network clustering problem is translated to a graph reduction problem, which is formulated as a minimization of distance from lumpability. The problem is a non-convex, mixed-integer optimization problem and only depends on the graph structure of the system. We provide a heuristic algorithm to identify clusters that are not only suboptimal but are also connected, that is, each cluster forms a connected induced subgraph in the network system.

KW - clustering algorithm

KW - Large-scale systems

KW - lumpa-bility

KW - model reduction

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DO - 10.1109/CDC40024.2019.9029349

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Y2 - 11 December 2019 through 13 December 2019

ER -

Structure-based clustering algorithm for model reduction of large-scale network systems

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