Two stochastic model predictive control algorithms, which are referred to as distributionally
robust model predictive control algorithms, are proposed in this article for a class of discrete …

In this letter we introduce a novel approach to distributionally robust optimal control that
supports online learning of the ambiguity set, while guaranteeing recursive feasibility. We …

This letter studies the problem of distributionally robust model predictive control (MPC) using
total variation distance ambiguity sets. For a discrete-time linear system with additive …

We present a distributionally robust stochastic model predictive control scheme for linear
discrete-time systems subject to unbounded additive disturbance. We consider joint chance …

Abstract Model predictive control (MPC) is an effective approach to control multivariable
dynamic systems with constraints. Most real dynamic models are however affected by plant …

This paper presents a distributionally robust stochastic model predictive control (SMPC)
approach for linear discrete-time systems subject to unbounded and correlated additive …

To address the issue of inaccurate distributions in discrete-time stochastic systems, a
minimax linear quadratic control method using the Wasserstein metric is proposed. Our …

We present a novel data-driven distributionally robust Model Predictive Control formulation
for unknown discrete-time linear time-invariant systems affected by unknown and possibly …

We study the problem of refining satisfiability bounds for partially-known stochastic systems
against planning specifications defined using syntactically co-safe Linear Temporal Logic …

Distributionally robust control (DRC) aims to effectively manage distributional ambiguity in
stochastic systems. While most existing works address inaccurate distributional information …