We study model predictive control (MPC) problems for stochastic LTI systems, where the
noise distribution is unknown, compactly supported, and only observable through a limited …

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 …

We present a stochastic model predictive control (SMPC) framework for linear systems
subject to possibly unbounded disturbances. State-of-the-art SMPC approaches with closed …

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 …

Wasserstein distributionally robust control (WDRC) is an effective method for addressing
inaccurate distribution information about disturbances in stochastic systems. It provides …

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

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

We present a stochastic predictive control framework for nonlinear systems subject to
unbounded process noise with closed-loop guarantees. First, we first provide a conceptual …

This paper proposes an iterative distributionally robust model predictive control (MPC)
scheme to solve a risk-constrained infinite-horizon optimal control problem. In each iteration …