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 …

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 …

This paper presents a novel approach to addressing the distributionally robust nonlinear
model predictive control (DRNMPC) problem. Current literature primarily focuses on the …

This paper is motivated by addressing open questions in distributionally robust chance-
constrained programs (DRCCP) using the popular Wasserstein ambiguity sets. Specifically …

Differential dynamic programming (DDP) is a popular technique for solving nonlinear
optimal control problems with locally quadratic approximations. However, existing DDP …

Increasing the adoption of alternative technologies is vital to ensure a successful transition
to net-zero emissions in the manufacturing sector. However, existing models are limited in …

This paper presents a fully risk-aware model predictive control (MPC) framework for chance-
constrained discrete-time linear control systems with process noise. Conditional value-at …

This paper presents a distributionally robust data-driven model predictive control (MPC)
framework for discrete-time linear systems with additive disturbances, while assuming the …

We introduce a novel data-driven method to mitigate the risk of cascading failures in delayed
discrete-time Linear Time-Invariant (LTI) systems. Our approach involves formulating a …