For prediction and real-time control tasks, machine-learning (ML)-based digital twins are
frequently employed. However, while these models are typically accurate, they are custom …

This paper reviews the current techniques used in energy management systems to optimize
energy schedules into microgrids, accounting for uncertainties for various time frames (day …

We present differentiable predictive control (DPC) as a deep learning-based alternative to
the explicit model predictive control (MPC) for unknown nonlinear systems. In the DPC …

This paper proposes an Explicit Model Predictive Control (eMPC) for the energy
management of an e-vehicle charging station fueled by a photovoltaic plant (PV), a battery …

This paper provides a comprehensive review of machine learning strategies and
optimization formulations employed in energy management systems (EMS) tailored for plug …

Explicit machine learning-based model predictive control (explicit ML-MPC) has been
developed to reduce the real-time computational demands of traditional ML-MPC. However …

In this contribution, we present a high-fidelity dynamic model of an industrial dividing wall
column and the application of explicit model predictive control for its regulation. Our study …

The distributed control system architecture strikes a balance between the decentralized
control system architecture, where subsystem interactions are unaccounted for, and the …

Chemical process operation optimization aims at obtaining the optimal operating set-points
by real-time solution of an optimization problem that embeds a steady-state model of the …

Explicit model predictive control is an established methodology for the offline determination
of the optimal control policy for linear discrete time-invariant systems with linear constraints …