This paper proposes a clustered model reduction method for semistable positive linear
systems evolving over directed networks. In this method, we construct a set of clusters, ie …

We present a model reduction technique for a class of nonlinear ordinary differential
equation (ODE) models of heterogeneous systems, where heterogeneity is expressed in …

In this paper we present an efficient model order reduction method for multi-agent systems
with Laplacian-based dynamics. The method combines an established model order …

We propose a dissipativity-preserving structured model reduction method for distributed
control systems. As a fundamental tool to develop structured model reduction, we first …

In this paper, we propose a clustered model reduction method for interconnected second-
order systems evolving over undirected networks, which we call second-order networks. In …

This paper proposes a design method for low-dimensional linear functional observers based
on a model reduction approach. In contrast to existing methods for designing approximate …

In this work, we present some results coming from the comparison of two classes of
problems of optimal graph partitioning. In the first class of problem, called further constrained …

In this paper, towards efficient state estimation for large-scale linear systems, we propose a
novel framework of low-dimensional functional state observers, which we call a projective …

Given any positive integer r, our objective is to develop a strategy for grouping the states of a
n-node network into r≤ n distinct non-overlapping groups. The criterion for this partitioning …

This paper proposes a singular perturbation approximation that preserves system passivity
and an interconnection topology among subsystems. In the first half of this paper, we …