pith. sign in

Modal Barriers to Controllability in Networks with Linearly-Coupled Homogeneous Subsystems

1 Pith paper cite this work. Polarity classification is still indexing.

1 Pith paper citing it
abstract

The controllability of networks comprising homogeneous multi-input multi-output linear subsystems with linear couplings among them is examined, from a modal perspective. The eigenvalues of the network model are classified into two groups: 1) network-invariant modes, which have very high multiplicity regardless of the network's topology; and 2) special-repeat modes, which are repeated for only particular network topologies and have bounded multiplicity. Characterizations of both types of modes are obtained, in part by drawing on decentralized-fixed-mode and generalized-eigenvalue concepts. We demonstrate that network-invariant modes necessarily prevent controllability unless a sufficient fraction of the subsystems are actuated, both in the network as a whole and in any weakly-connected partition. In contrast, the multiplicities of special-repeat modes have no influence on controllability. Our analysis highlights a distinction between built networks where subsystem interfaces may be unavoidable barriers to controllability, and multi-agent systems where protocols can be designed to ensure controllability.

fields

math.OC 1

years

2019 1

verdicts

UNVERDICTED 1

representative citing papers

Controllability of Kronecker Product Networks

math.OC · 2019-06-27 · unverdicted · novelty 5.0

Derives necessary and sufficient controllability conditions for Kronecker product networks of directed graphs and provides a corrected condition for higher-dimensional multi-agent systems.

citing papers explorer

Showing 1 of 1 citing paper.

  • Controllability of Kronecker Product Networks math.OC · 2019-06-27 · unverdicted · none · ref 30 · internal anchor

    Derives necessary and sufficient controllability conditions for Kronecker product networks of directed graphs and provides a corrected condition for higher-dimensional multi-agent systems.