Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

We consider the problem of optimal trajectory tracking for unknown systems. A novel data-
enabled predictive control (DeePC) algorithm is presented that computes optimal and safe …

This paper addresses the optimal control problem known as the linear quadratic regulator in
the case when the dynamics are unknown. We propose a multistage procedure, called …

In this article we study the problem of finite-time constrained optimal control of unknown
stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive …

We prove that the ordinary least-squares (OLS) estimator attains nearly minimax optimal
performance for the identification of linear dynamical systems from a single observed …

We study the performance of the certainty equivalent controller on Linear Quadratic (LQ)
control problems with unknown transition dynamics. We show that for both the fully and …

We consider the problem of learning a realization for a linear time-invariant (LTI) dynamical
system from input/output data. Given a single input/output trajectory, we provide finite time …

Stability is a fundamental property of dynamical systems, yet to this date it has had little
bearing on the practice of recurrent neural networks. In this work, we conduct a thorough …

This letter considers the problem of linear time-invariant (LTI) system identification using
input/output data. Recent work has provided non-asymptotic results on partially observed …

We address the problem of learning the parameters of a stable linear time invariant (LTI)
system with unknown latent space dimension, or order, from a single time—series of noisy …