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 …

This article provides an overview of certain direct data-driven control results, where control
sequences are computed from (noisy) data collected during offline control experiments …

In this letter we study an imitation and transfer learning setting for Linear Quadratic Gaussian
(LQG) control, where (i) the system dynamics, noise statistics and cost function are unknown …

This paper addresses the joint state estimation and control problems for unknown linear time-
invariant systems subject to both process and measurement noise. The aim is to redesign …

In this paper we provide direct data-driven expressions for the Linear Quadratic Regulator
(LQR), the Kalman filter, and the Linear Quadratic Gaussian (LQG) controller using a finite …

We study the problem of estimating the states of a linear system based on measured data.
We investigate the problem in both deterministic and stochastic settings. In the deterministic …

In this work, we derive dynamic output-feedback controllers that render the closed-loop
system externally positive. We begin by expressing the class of discrete-time, linear, time …

We present a model-based globally convergent policy gradient method (PGM) for linear
quadratic Gaussian (LQG) control. Firstly, we establish equivalence between optimizing …

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 …

In the field of machine learning, the quest to optimize the performance of machine learning
models while maintaining robustness against perturbations stands as a fundamental …