The linear quadratic regulator (LQR) problem is a cornerstone of control theory and a widely
studied benchmark problem. When a system model is not available, the conventional …

In this article, we discuss connections between sequential system identification and control
for linear time-invariant systems, often termed indirect data-driven control, as well as a …

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 …

We address the problem of designing a stabilizing closed-loop control law directly from input
and state measurements collected in an experiment. In the presence of a process …

This paper studies several problems related to quadratic matrix inequalities (QMIs), ie,
inequalities in the Loewner order involving quadratic functions of matrix variables. In …

In this article, we introduce a method to deal with the data-driven control design of nonlinear
systems. We derive conditions to design controllers via (approximate) nonlinearity …

Behavioral systems theory decouples the behavior of a system from its representation. A key
result is that, under a persistency of excitation condition, the image of a Hankel matrix …

The linear quadratic regulator (LQR) problem is a cornerstone of automatic control, and it
has been widely studied in the data-driven setting. The various data-driven approaches can …

In this article, we consider linear quadratic (LQ) control problem with process and
measurement noises. We analyze the LQ problem in terms of the average cost and the …

For systems with uncertain linear models, bounded additive disturbances and state and
control constraints, a robust model predictive control (MPC) algorithm incorporating online …