Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

The behavioral approach to systems theory, put forward 40 years ago by Jan C. Willems,
takes a representation-free perspective of a dynamical system as a set of trajectories. Till …

In this article, we propose a new method to obtain feedback controllers of an unknown
dynamical system directly from noisy input/state data. The key ingredient of our design is a …

Real-world networks often consist of millions of heterogenous elements that interact at
multiple timescales and length scales. The fields of statistical physics and control theory both …

This article provides an overview of a new approach to designing controllers for nonlinear
systems using data-driven control. Data-driven control is an important area of research in …

This paper studies the problem of data-driven control for discrete-time piecewise affine
(PWA) systems. Based on a sequel of sampled control inputs and states with satisfactory …

In this work, we compare the direct and indirect approaches to data-driven predictive control
of stochastic linear time-invariant systems. The distinction between the two approaches lies …

Self-triggered control, a well-documented technique for reducing the communication
overhead while ensuring desired system performance, is gaining increasing popularity …

Motivated by the goal of learning controllers for complex systems whose dynamics change
over time, we consider the problem of designing control laws for systems that switch among …

System identification learns mathematical models of dynamic systems starting from input–
output data. Despite its long history, such research area is still extremely active. New …