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 most recent work in the field of model-based design of catalytic reactors with a special
focus on the multi-level reactor design (MLRD) methodology is reviewed. The key idea of …

Stochastic model predictive control hinges on the online solution of a stochastic optimal
control problem. This paper presents a computationally efficient solution method for …

This paper addresses the problem of stochastic trajectory planning for 6-DOF spacecraft
close proximity in the presence of external disturbances and initial state uncertainties …

Global self-optimizing control (gSOC) aims to identify optimal controlled variables (CVs) that
minimize the average economic cost when uncertainties vary in the whole distribution …

This paper presents a framework for reliable, robust, and resilient system design, addressing
the need for acceptable performance not only to be provided under expected conditions, but …

Background Micro-organisms play an important role in various industrial sectors (including
biochemical, food and pharmaceutical industries). A profound insight in the biochemical …

Realistic industrial process is usually a dynamic process with uncertainty. Chance
constraints are applicable to industrial process modeling under uncertain conditions, where …

We study the problem of performance optimization of closed-loop control systems with
unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for …

Abstract Model-based optimization under consideration of uncertainty is an important and
active research topic. In order to handle uncertain model and process parameters for the …