The energy sector is undergoing a paradigm shift among all the stages, from generation to
the consumer end. The affordable, flexible, secure supply–demand balance due to an …

Building energy management has been recognized as of significant importance on
improving the overall system efficiency and reducing the greenhouse gas emission …

This paper presents a deep learning-based model predictive control (MPC) method for
operational supply chain optimization in real time. The method follows an offline-online …

Abstract Mixed integer Model Predictive Control (MPC) problems arise in the operation of
systems where discrete and continuous decisions must be taken simultaneously to …

Process control and optimization have been widely used to solve decision-making problems
in chemical engineering applications. However, identifying and tuning the best solution …

This paper introduces a novel model-free approach to synthesize virtual sensors for the
estimation of dynamical quantities that are unmeasurable at runtime but are available for …

Solving large-scale optimization problems with numerous constraints and optimization
variables is a challenging task. Partial explicit MPC enables solving the large-scale …

Distributed electric drive vehicles offer maneuver-ability but face stability challenges under
different driving conditions. Model Predictive Control (MPC) algorithms can improve lateral …

Stochastic Model Predictive Control (MPC) gained popularity thanks to its capability of
overcoming the conservativeness of robust approaches, at the expense of a higher …

Model predictive control (MPC) has been widely used to control and operate complex
systems. However, the efficient implementation of MPC depends on the efficient solution of …