Synchronization preserving model reduction of multi-agent network systems by eigenvalue assignments

Yu, Lanlin; Cheng, Xiaodong; Scherpen, Jacquelien M.A.; Xiong, Junlin

doi:10.1109/cdc40024.2019.9029857

Cited by 7 publications

(9 citation statements)

References 24 publications

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“…Indirect Network Reduction Methods. Different from the mechanisms of clustering and aggregation that directly produce a reduced network, indirected methods in (91,38,92,93) seek a structure-preserving reduced-order model using a two-step procedure: reduction and transformation. In the reduction step, a lower-dimensional model of a given largescale network system is constructed by using conventional model reduction methods, e.g.…”

Section: Network Reduction Towardsmentioning

confidence: 99%

“…Similarly, an eigenvalue assignment approach, (93), directly selects a subset of the Laplacian spectrum of the original network to be the eigenvalues of the Laplacian matrix for the reduced network. By doing so, certain properties of original network such as stability and synchronization can be preserved through the reduction process.…”

Section: Network Reduction Towardsmentioning

confidence: 99%

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Model Reduction Methods for Complex Network Systems

Cheng

Scherpen

2021

Annu. Rev. Control Robot. Auton. Syst.

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Network systems consist of subsystems and their interconnections and provide a powerful framework for the analysis, modeling, and control of complex systems. However, subsystems may have high-dimensional dynamics and a large number of complex interconnections, and it is therefore relevant to study reduction methods for network systems. Here, we provide an overview of reduction methods for both the topological (interconnection) structure of a network and the dynamics of the nodes while preserving structural properties of the network. We first review topological complexity reduction methods based on graph clustering and aggregation, producing a reduced-order network model. Next, we consider reduction of the nodal dynamics using extensions of classical methods while preserving the stability and synchronization properties. Finally, we present a structure-preserving generalized balancing method for simultaneously simplifying the topological structure and the order of the nodal dynamics. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 3, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Network Reduction Towardsmentioning

confidence: 99%

Section: Network Reduction Towardsmentioning

confidence: 99%

Model Reduction Methods for Complex Network Systems

Cheng

Scherpen

2021

Annu. Rev. Control Robot. Auton. Syst.

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“…In the line of works [42,41], a reduction method for network systems composed of higher-dimensional dissipative subsystems is presented in [43], where the subsystems are reduced via block-diagonally orthogonal projection, while the network structure is simplified using clustering. In [16], the balancing method, for the first time, is applied for reducing the interconnection structure of networks with diffusively coupled vertices, and more extensions are found in [78,77] based on eigenvalue assignment and moment matching. In [21], the idea in [16] is further developed and applied to general networks of the form (13).…”

Section: Simultaneously Reduction Of Network Structure and Subsystemsmentioning

confidence: 99%

Reduced-Order Modeling of Large-Scale Network Systems

Cheng,

Scherpen,

Trentelman

2021

Preprint

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Large-scale network systems describe a wide class of complex dynamical systems composed of many interacting subsystems. A large number of subsystems and their high-dimensional dynamics often result in highly complex topology and dynamics, which pose challenges to network management and operation. This chapter provides an overview of reduced-order modeling techniques that are developed recently for simplifying complex dynamical networks. In the first part, clustering-based approaches are reviewed, which aim to reduce the network scale, i.e., find a simplified network with a fewer number of nodes. The second part presents structure-preserving methods based on generalized balanced truncation, which can reduce the dynamics of each subsystem.

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“…In the line of works [42,41], a reduction method for network systems composed of higher-dimensional dissipative subsystems is presented in [43], where the subsystems are reduced via block-diagonal orthogonal projection, while the network structure is simplified using clustering. In [16], the balancing method, for the first time, is applied for reducing the interconnection structure of networks with diffusively coupled vertices, and more extensions are found in [78,77] based on eigenvalue assignment and moment-matching. In [21], the idea in [16] is further developed and applied to general networks of the form (11.13).…”

Section: Simultaneously Reduction Of Network Structure and Subsystemsmentioning

confidence: 99%