Abstract Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is
studied in various fields such as engineering, computer science, economics, and …

Performance of control systems interacting over a shared communication network is tightly
coupled with how the network provides services and distributes resources. Novel networking …

This paper presents a synthesis method for robust, regret optimal control. The plant is
modeled in discrete‐time by an uncertain linear time‐invariant (LTI) system. An optimal non …

Inspired by competitive policy designs approaches in online learning, new control
paradigms such as competitive-ratio and regret-optimal control have been recently …

The setting of an agent making decisions under uncertainty and under dynamic constraints
is common for the fields of optimal control, reinforcement learning, and recently also for …

Towards bridging classical optimal control and online learning, regret minimization has
recently been proposed as a control design criterion. This competitive paradigm penalizes …

This paper studies online solutions for regretoptimal control in partially observable systems
over an infinitehorizon. Regret-optimal control aims to minimize the difference in LQR cost …

We present an online learning analysis of minimax adaptive control for the case where the
uncertainty includes a finite set of linear dynamical systems. Precisely, for each system …

Online learning algorithms for dynamical systems provide finite time guarantees for control
in the presence of sequentially revealed cost functions. We pose the classical linear …

This paper brings the concept of" optimism" to the new and promising framework of online
Non-stochastic Control (NSC). Namely, we study how can NSC benefit from a prediction …