Model predictive control (MPC) of nonlinear systems suffers a trade-off between model
accuracy and real-time compu-tational burden. This thesis presents an interpretative and …

This article proposes a novel coordinate-descent augmented-Lagrangian (CDAL) solver for
linear, possibly parameter-varying, model predictive control (MPC) problems. At each …

Model predictive control (MPC) for nonlinear systems suffers a trade-off between the model
accuracy and real-time computational burden. One widely used approximation method is the …

Explicit model predictive control (MPC) addresses the problem of removing one of the main
drawbacks of MPC, namely the need to solve a mathematical program on line to compute …

This paper presents a rapid-prototype Model Predictive Control (MPC) tool based on the
gPROMS platform, with the support for the whole MPC design workflow. The gPROMS-MPC …

In this study, we are concerned with nonlinear model predictive control (NMPC) schemes
that, through the linear parameter-varying (LPV) formulation, nonlinear systems can be …

In model predictive control (MPC), the control input is computed by solving a constrained
finite-time optimal control (CFTOC) problem at each sample in the control loop. The main …

Linear model predictive control (MPC) can be currently deployed at outstanding speeds,
thanks to recent progress in algorithms for solving online the underlying structured quadratic …

In model predictive control fast and reliable quadratic programming solvers are of
fundamental importance. The inherent structure of the subsequent optimal control problems …

This paper presents a novel approach to solve linear and nonlinear model predictive control
(MPC) problems that requires small memory footprint and throughput and is particularly …