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 …

This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

Data-driven control approaches for the minimization of energy consumption of buildings
have the potential to significantly reduce deployment costs and increase the uptake of …

In a recent paper it was shown how a matrix S-lemma can be applied to construct controllers
from noisy data. The current paper complements these results by proving a matrix version of …

This paper proposes a data-driven control framework to regulate an unknown stochastic
linear dynamical system to the solution of a stochastic convex optimization problem. Despite …

Unknown-input observers (UIOs) allow for estimation of the states of an LTI system without
knowledge of all inputs. In this letter, we provide a novel data-driven UIO based on …

This paper proposes a data-driven framework to solve time-varying optimization problems
associated with unknown linear dynamical systems. Making online control decisions to …

The exact pole placement problem concerns computing a static feedback law for a linear
dynamical system that will assign its poles at a set of pre-specified locations. This is a classic …

Control using quantized feedback is a fundamental approach to system synthesis with
limited communication capacity. In this paper, we address the stabilization problem for …

For linear systems, many data-driven control methods rely on the behavioral framework,
using historical data of the system to predict the future trajectories. However, measurement …