In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …

It is well-documented how artificial intelligence can have (and already is having) a big
impact on chemical engineering. But classical machine learning approaches may be weak …

We present CasADi, an open-source software framework for numerical optimization. CasADi
is a general-purpose tool that can be used to model and solve optimization problems with a …

This paper provides a review of model predictive control (MPC) methods with active
uncertainty learning. System uncertainty poses a key theoretical and practical challenge in …

This paper investigates a new class of modifier-adaptation schemes to overcome plant-
model mismatch in real-time optimization of uncertain processes. The main contribution lies …

In this study, a model order reduction (MOR) technique is proposed to address the
challenges of controlling large-scale problems for model predictive control (MPC) …

Information-theoretic multi-time-scale partially observable systems with inspiration from
leukemia treatment - ScienceDirect Skip to main contentSkip to article Elsevier logo …

Model predictive control (MPC) is one of the most effective methods of dealing with
constrained control problems. Nevertheless, the uncertainty of the control system poses …

控制系统中存在的不确定性为其性能优化带来诸多问题. 自适应控制和鲁棒控制是针对系统存在
的不确定性而采取的不同设计策略; 前者没有充分考虑系统的未建模动态, 而后者往往是针对不 …

The integration of renewables into electrical grids calls for novel control schemes, which
usually are model based. Classically, for power systems parameter estimation and …