Learning the exact topology of undirected consensus networks

Talukdar, Saurav; Deka, Deepjyoti; Attree, Sandeep; Materassi, Donatello; Salapaka, Murti V.

doi:10.1109/cdc.2017.8264533

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…Preliminary work subsumed by this article instantiated to various application domains have appeared in [22] for power grid networks, [26] for thermal dynamics of buildings and [27] for consensus networks. This article is a detailed version with complete proofs with a presentation from a general linear dynamical system perspective and explores connections with physical laws.…”

Section: Introductionmentioning

confidence: 99%

Physics informed topology learning in networks of linear dynamical systems

et al. 2020

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Learning influence pathways of a network of dynamically related processes from observations is of considerable importance in many disciplines. In this article, influence networks of agents which interact dynamically via linear dependencies are considered. An algorithm for the reconstruction of the topology of interaction based on multivariate Wiener filtering is analyzed. It is shown that for a vast and important class of interactions, that respect flow conservation, the topology of the interactions can be exactly recovered. The class of problems where reconstruction is guaranteed to be exact includes power distribution networks, dynamic thermal networks and consensus networks. The efficacy of the approach is illustrated through simulation and experiments on consensus networks, IEEE power distribution networks and thermal dynamics of buildings.

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Section: Introductionmentioning

confidence: 99%

Physics informed topology learning in networks of linear dynamical systems

et al. 2020

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“…Among other methods for connectivity determination, a recent one can be found in [32], where authors combine a switching signal that determines the network topology with the consensus algorithm in order to exclude spoofed nodes and achieve agreement. Another interesting approach is suggested in [33]. Authors used frequency response of Wiener filters to pinpoint spurious links in the network, and assure algorithm convergence using only valid links in linear consensus.…”

Section: A Literature Overviewmentioning

confidence: 99%

Consensus-Based Distributed Connectivity Control in Multi-Agent Systems

Griparic

Polić

Križmančić

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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This paper addresses a distributed connectivity control problem in networked multi-agent systems. The system communication topology is controlled through the algebraic connectivity measure, the second smallest eigenvalue of the communication graph Laplacian. The algebraic connectivity is estimated locally in a decentralized manner through a trustbased consensus algorithm, in which the agents communicate the perceived quality of the communication links in the system with their set of neighbors. In the presented approach, link qualities represent the weights of the communication graph from which the adjacency matrix is estimated. The Laplacian matrix and its eigenvalues, including the algebraic connectivity, are then calculated from this local estimate of the global adjacency matrix. A method for network topology control is proposed, which creates and deletes communication links based on the Albert-Barabási probabilistic model, depending on the estimated and referenced connectivity level. The proposed algebraic connectivity estimation and connectivity maintenance strategy have been validated both in simulation and on a physical robot swarm, demonstrating the method performance under varying initial topology of the communication graph, different multi-agent system sizes, in various deployment scenarios, and in the case of agent failure.

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“…These works primarily focus on directed networks of linear dynamical systems or assume uncorrelated inputs. Structure learning in undirected linear systems has been explored recently for radial networks [15] and loopy networks [16], [17] in power systems and multi-agent distributed systems. These articles utilize properties of multivariate Wiener filters to provide consistent structure estimation.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Identification of a Thermal Network in Multi-Zone Building

Doddi

Talukdar

Deka

et al. 2018

2018 IEEE Conference on Decision and Control (CDC)

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System identification of smart buildings is necessary for their optimal control and application in demand response. The thermal response of a building around an operating point can be modeled using a network of interconnected resistors with capacitors at each node/zone called RC network. The development of the RC network involves two phases: obtaining the network topology, and estimating thermal resistances and capacitance's. In this article, we present a provable method to reconstruct the interaction topology of thermal zones of a building solely from temperature measurements. We demonstrate that our learning algorithm accurately reconstructs the interaction topology for a 5 zone office building in EnergyPlus with real-world conditions. We show that our learning algorithm is able to recover the network structure in scenarios where prior research prove insufficient.

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Learning the exact topology of undirected consensus networks

Cited by 4 publications

References 32 publications

Physics informed topology learning in networks of linear dynamical systems

Physics informed topology learning in networks of linear dynamical systems

Consensus-Based Distributed Connectivity Control in Multi-Agent Systems

Data-Driven Identification of a Thermal Network in Multi-Zone Building

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