In a recent paper it was shown how a matrix S-lemma can be applied to construct controllers
from noisy data. The current paper complements these results by proving a matrix version of …

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 paper presents a risk-informed data-driven safe control design approach for a class of
stochastic uncertain nonlinear discrete-time systems. The nonlinear system is modeled …

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 …

In this article, we present a data-driven learning model predictive control (MPC) scheme for
chance-constrained Markov jump systems with unknown switching probabilities. Using …

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 …

This chapter studies the robustness of reinforcement learning for discrete-time linear
stochastic systems with multiplicative noise evolving in continuous state and action spaces …

We propose an interaction-aware stochastic model predictive control (MPC) strategy for lane
merging tasks in automated driving. The MPC strategy is integrated with an online learning …

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

The paper studies identification of linear systems with multiplicative noise from multiple-
trajectory data. An algorithm based on the least-squares method and multiple-trajectory data …