12 research outputs found
Introduction to the Special Issue on Approaches to Control Biological and Biologically Inspired Networks
The emerging field at the intersection of quantitative biology, network
modeling, and control theory has enjoyed significant progress in recent years.
This Special Issue brings together a selection of papers on complementary
approaches to observe, identify, and control biological and biologically
inspired networks. These approaches advance the state of the art in the field
by addressing challenges common to many such networks, including high
dimensionality, strong nonlinearity, uncertainty, and limited opportunities for
observation and intervention. Because these challenges are not unique to
biological systems, it is expected that many of the results presented in these
contributions will also find applications in other domains, including physical,
social, and technological networks
Minimal Driver Nodes for Structural Controllability of Large-Scale Dynamical Systems: Node Classification
This paper considers the problem of minimal control inputs to affect the
system states such that the resulting system is structurally controllable. This
problem and the dual problem of minimal observability are claimed to have no
polynomial-order exact solution and, therefore, are NP-hard. Here, adopting a
graph-theoretic approach, this problem is solved for general nonlinear (and
also structure-invariant) systems and a P-order solution is proposed. In this
direction, the dynamical system is modeled as a directed graph, called
\textit{system digraph}, and two types of graph components are introduced which
are tightly related with structural controllability. Two types of nodes which
are required to be affected (or driven) by an input, called \textit{driver
nodes}, are defined, and minimal number of these driver nodes are obtained.
Polynomial-order complexity of the given algorithms to solve the problem
ensures applicability of the solution for analysis of large-scale dynamical
systems. {The structural results in this paper are significant as compared to
the existing literature which offer approximate and computationally
less-efficient, e.g. Gramian-based, solutions for the problem, while this paper
provides exact solution with lower computational complexity and applicable for
controllability analysis of nonlinear systems.Comment: accepted at IEEE Systems Journa