This paper addresses problems on the structural design of large-scale control systems. An
efficient and unified framework is proposed to select the minimum number of manipulated …

In this paper, we focus on sensor placement in linear dynamic estimation, where the
objective is to place a small number of sensors in a system of interdependent states so to …

In recent years, complex networks have gained increasing attention in different fields of
science and engineering. The problem of controlling these networks is an interesting and …

In this paper, we address the robust minimal controllability problem, where the goal is, given
a linear time-invariant system, to determine a minimal subset of state variables to be …

In this paper, we focus on applications in machine learning, optimization, and control that
call for the resilient selection of a few elements, eg features, sensors, or leaders, against a …

Physiological signals are often spatiotemporal dependent. Some of these signals include
electroencephalogram (EEG), electromyogram (EMG) or electrocardiogram (ECG) signals …

This paper considers the problem of controlling a linear time-invariant network by means of
(possibly) time-varying set of control nodes. As control metric, we adopt the worst-case input …

In this paper, we focus on sensor selection, that is, determine the minimum number of state
variables that need to be measured, to monitor the evolution of the entire biological system …

This paper aims to establish explicit relationships between the controllability degree of a
network, that is, the control energy required to move the network between different states …

In this paper, we study the minimal cost constrained input-output (I/O) and control
configuration co-design problem. Given a linear time-invariant plant, where a collection of …