As we move towards safety-critical cyber-physical systems that operate in non-stationary
and uncertain environments, it becomes crucial to close the gap between classical optimal …

We present an optimisation-based method for synthesising a dynamic regret optimal
controller for linear systems with potentially adversarial disturbances and known or …

In this article, we consider estimation and control in linear dynamical systems from the
perspective of regret minimization. Unlike most prior work in this area, we focus on the …

Thompson Sampling (TS) is an efficient method for decision-making under uncertainty,
where an action is sampled from a carefully prescribed distribution which is updated based …

We consider control from the perspective of competitive analysis. Unlike much prior work on
learning-based control, which focuses on minimizing regret against the best controller …

We consider the infinite-horizon, discrete-time full-information control problem. Motivated by
learning theory, as a criterion for controller design we focus on regret, defined as the …

This paper proposes a distributionally robust approach to regret optimal control of discrete-
time linear dynam-ical systems with quadratic costs subject to a stochastic additive …

The H_∞ synthesis approach is a cornerstone robust control design technique, but is known
to be conservative in some cases. The objective of this paper is to quantify the additional …

In this work, we focus on the design of optimal controllers that must comply with an
information structure. State-of-the-art approaches do so based on the H_2 or H_∞ norm to …

This paper presents a framework for Wasserstein distributionally robust (DR) regret-optimal
(RO) control in the context of partially observable systems. DR-RO control considers the …