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 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 …
I Markovsky, L Huang, F Dörfler - IEEE Control Syst., 2022 - imarkovs.github.io
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 …
Y Zheng, N Li - IEEE Control Systems Letters, 2020 - ieeexplore.ieee.org
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 …
Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include …
This paper studies a class of partially observed Linear Quadratic Gaussian (LQG) problems with unknown dynamics. We establish an end-to-end sample complexity bound on learning …
S Skogestad - Annual Reviews in Control, 2023 - Elsevier
The paper explores the standard advanced control elements commonly used in industry for designing advanced control systems. These elements include cascade, ratio, feedforward …
Motivated by vision-based control of autonomous vehicles, we consider the problem of controlling a known linear dynamical system for which partial state information, such as …
E Hazan, K Singh - arXiv preprint arXiv:2211.09619, 2022 - arxiv.org
This text presents an introduction to an emerging paradigm in control of dynamical systems and differentiable reinforcement learning called online nonstochastic control. The new …