We present a methodology to learn explicit Model Predictive Control (eMPC) laws from
sample data points with tunable complexity. The learning process is cast in a special Neural …

Predictive control is a flexible control methodology that can optimize performance while
satisfying current and voltage constraints. Its application in the power electronics domain is …

The increased uncertainty due to the integration of stochastic renewable energy sources
necessitates solving Optimal Power Flow (OPF) problems repeatedly and with greater …

This paper focuses on the (re-) construction of the optimal solution for the multi-parameter
quadratic programming (mpQP) problems. optimization problems of this nature are widely …

Abstract Computing a Model Predictive Controller (MPC) requires high computational loads,
which typically challenges its implementation in embedded hardware. Recently, learning …

This paper focuses on the geometric properties of the Piece-Wise Affine (PWA) feedback
function as they appear from the optimal solution of the multi-parameter quadratic …

The application of model predictive control (MPC) is investigated on a buck dc-dc converter,
featuring a region elimination technique to alleviate its computational complexity. More …

This paper deals with the trajectory tracking problem of a tilt-rotor unmanned aerial vehicle
carrying a suspended load. An explicit model predictive control (eMPC) based on …

This thesis is situated at the crossroads between machine learning and control engineering.
Our contributions are both theoretical, through proposing a new uncertainty quantification …