Efficient Computation of H2 Performance on Series-Parallel Networks

Abstract

Series-parallel networks are a class of graphs on which many NP-hard problems have tractable solutions. In this paper, we examine performance measures on leader-follower consensus on series-parallel networks. We show that a distributed computation of the ${\mathcal{H}}_2$ norm can be done efficiently on this system by exploiting a decomposition of the network into atomic elements and composition rules. Lastly, we examine the problem of adaptively re-weighting the network to optimize the ${\mathcal{H}}_2$ norm, and show that it can be done with similar complexity.

6 pages, 5 figures. To appear in proceedings of the 2019 American Control Conference

References in corpus (1)

• Optimal mass transport and kernel density estimation for state-dependent networked dynamic systems

Cited by in corpus (1)

• ${\mathcal{H}}_2$ performance of series-parallel networks: A compositional perspective