We present a case study applying learning-based distributionally robust model predictive
control to highway motion planning under stochastic uncertainty of the lane change behavior …

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 …

This letter studies optimal control problems of unknown linear systems subject to stochastic
disturbances of uncertain distribution. Uncertainty about the stochastic disturbances is …

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 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 …

This paper considers a risk-constrained infinite-horizon optimal control problem and
proposes to solve it in an iterative manner. Each iteration of the algorithm generates a …

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

This paper addresses the limitations of standard uncertainty models, eg, robust (norm-
bounded) and stochastic (one fixed distribution, eg, Gaussian), and proposes to model …

A data-driven MPC scheme is proposed to safely control constrained stochastic linear
systems using distributionally robust optimization. Distributionally robust constraints based …