In this paper, we discuss the model predictive control algorithms that are tailored for
uncertain systems. Robustness notions with respect to both deterministic (or set based) and …

The biopharmaceutical industry is rapidly advancing, driven by the need for cutting‐edge
technologies to meet the growing demand for life‐saving treatments. In this context, Model …

In this article, the problem of optimal time-varying attitude tracking control for rigid spacecraft
with system constraints and unknown additive disturbances is considered. Through the …

Gaussian process (GP) regression has been widely used in supervised machine learning
due to its flexibility and inherent ability to describe uncertainty in function estimation. In the …

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 …

A supervised learning framework is proposed to approximate a model predictive controller
(MPC) with reduced computational complexity and guarantees on stability and constraint …

In this article, we present a nonlinear robust model predictive control (MPC) framework for
general (state and input dependent) disturbances. This approach uses an online …

The paper presents a tube model predictive control (MPC) scheme of continuous-time
nonlinear systems based on robust control invariant sets with respect to unknown but …

This paper deals with the robustness of Model Predictive Controllers for constrained
uncertain nonlinear systems. The uncertainty is assumed to be modeled by a state and input …

Model Predictive Control (MPC) is an area in rapid development with respect to both
theoretical and application aspects. The former petrochemical applications of MPC were …