This article deals with data-driven stability analysis and feedback stabilization of linear input–
output systems in autoregressive (AR) form. We assume that noisy input–output data on a …

We consider the feedback linearization problem, and contribute with a new method that can
learn the linearizing controller from a library (a dictionary) of candidate functions. When the …

When dealing with unknown systems, data can be used to directly learn controllers with
desirable features, thus bypassing system identification. In this paper we present strategies …

Imperfect models lead to imperfect controllers and deriving accurate models from first
principles or system identification is especially challenging in networked systems. Instead …

Studying structural properties of linear dynamical systems through invariant subspaces is
one of the key contributions of the geometric approach to system theory. In general, a model …

In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control problem from a
behavioral perspective. Motivated by the suitability of behavioral models for data-driven …

In this paper, we consider optimal control problems (OCPs) applied to large-scale linear
dynamical systems with a large number of states and inputs. We attempt to reduce such …

In this paper, we consider optimal control problems (OCPs) applied to large-scale linear
dynamical systems with many states and many inputs and a quadratic objective function. We …

The traditional approach for the control of dynamical systems relies on the availability of a
model describing the system to be controlled. Typically, a model is derived from first …