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Average State Estimation in Large-scale Clustered Network Systems

Muhammad Umar Niazi 1 Carlos Canudas de Wit 1 Alain Kibangou 1
1 NECS-POST - Systèmes Commandés en Réseau
Inria Grenoble - Rhône-Alpes, GIPSA-PAD - GIPSA Pôle Automatique et Diagnostic
Abstract : For the monitoring of large-scale clustered network systems (CNS), it suffices in many applications to know the aggregated states of given clusters of nodes. This paper provides necessary and sufficient conditions such that the average states of the pre-specified clusters can be reconstructed and/or asymptotically estimated. To achieve computational tractability, the notions of average observability (AO) and average detectability (AD) of the CNS are defined via the projected network system, which is of tractable dimension and is obtained by aggregating the clusters. The corresponding necessary and sufficient conditions of AO and AD are provided and interpreted through the underlying structure of the induced subgraphs and the induced bipartite subgraphs, which capture the intra-cluster and inter-cluster topologies of the CNS, respectively. Moreover, the design of an average state observer whose dimension is minimum and equals the number of clusters in the CNS is presented.
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Submitted on : Monday, March 30, 2020 - 4:34:44 PM
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Muhammad Umar Niazi, Carlos Canudas de Wit, Alain Kibangou. Average State Estimation in Large-scale Clustered Network Systems. IEEE Transactions on Control of Network Systems, IEEE, 2020, 7 (4), pp.1736 - 1745. ⟨10.1109/TCNS.2020.2999304⟩. ⟨hal-02524982⟩



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