Composition Rules for Strong Structural Controllability and Minimum Input Problem in Diffusively-Coupled Networks

TL;DR

This paper develops new theoretical conditions and a composition rule for analyzing strong structural controllability in diffusively-coupled networks, enabling efficient determination of minimal external inputs needed for control.

Contribution

It introduces a composition rule for strong structural controllability, extends analysis to pactus graphs, and proposes an algorithm for minimal input node selection.

Findings

Established necessary and sufficient conditions for controllability with self-loops.

Developed a composition rule for larger network analysis.

Proposed an algorithm with approximate polynomial complexity.

Abstract

This paper presents new results and reinterpretation of existing conditions for strong structural controllability in a structured network determined by the zero/non-zero patterns of edges. For diffusively-coupled networks with self-loops, we first establish a necessary and sufficient condition for strong structural controllability, based on the concepts of dedicated and sharing nodes. Subsequently, we define several conditions for strong structural controllability across various graph types by decomposing them into disjoint path graphs. We further extend our findings by introducing a composition rule, facilitating the analysis of strong structural controllability in larger networks. This rule allows us to determine the strong structural controllability of connected graphs called pactus graphs (a generalization of the well-known cactus graph) by consideration of the strong structural controllability of its disjoint component graphs. In this process, we introduce the notion of a component input node, which is a state node that functions identically to an external input node. Based on this concept, we present an algorithm with approximate polynomial complexity to determine the minimum number of external input nodes required to maintain strong structural controllability in a diffusively-coupled network with self-loops.