Recent successes in the field of machine learning, as well as the availability of increased
sensing and computational capabilities in modern control systems, have led to a growing …

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

Many studies have proven that the building sector can significantly benefit from replacing the
current practice rule-based controllers (RBC) by more advanced control strategies like …

This paper describes nonlinear methods in model building, dynamic data reconciliation, and
dynamic optimization that are inspired by researchers and motivated by industrial …

To facilitate the real-time implementation of nonlinear model predictive control (NMPC), this
paper proposes a deep learning-based NMPC scheme, in which the NMPC law is …

In this article, we propose a novel framework for approximating the MPC policy for linear
parameter-varying systems using supervised learning. Our learning scheme guarantees …

Recently, there has been an intensive research in the area of Model Predictive Control
(MPC) for buildings. The key principle of MPC is a trade-off between energy savings and …

In this paper, we propose a novel framework for approximating the explicit MPC law for
linear parameter-varying systems using supervised learning. In contrast to most existing …

In this article, we propose a framework for the stability verification of mixed-integer linear
programming (MILP) representable control policies. This framework compares a fixed …

Mixed-integer convex programming (MICP) has seen significant algorithmic and hardware
improvements with several orders of magnitude solve time speedups compared to 25 years …