Structure-based Clustering Algorithm for Model Reduction of Large-scale Network Systems

Muhammad Umar B. Niazi (1) , Xiaodong Cheng (2) , Carlos Canudas de Wit (1) , Jacquelien M A Scherpen (3)

1 NECS-POST - Systèmes Commandés en Réseau
2 TU/e - Eindhoven University of Technology [Eindhoven]
3 University of Groningen [Groningen]

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.

Mots clés: Large-scale systems; Model reduction; Lumpability; Clustering algorithms

Domaines: Système multi-agents [cs.MA]; Systèmes dynamiques [math.DS]; Optimisation et contrôle [math.OC]; Automatique; Systèmes et contrôle [cs.SY]; Réseaux sociaux et d'information [cs.SI]; Modélisation et simulation

Muhammad Umar B. Niazi, Xiaodong Cheng, Carlos Canudas de Wit, Jacquelien M A Scherpen. Structure-based Clustering Algorithm for Model Reduction of Large-scale Network Systems. 58th IEEE Conference on Decision and Control, IEEE, Dec 2019, Nice, France. ⟨hal-02278884v1⟩