Self-optimizing control is a strategy for selecting controlled variables. It is distinguished by
the fact that an economic objective function is adopted as a selection criterion. The aim is to …

In multi-parametric programming, an optimization problem is solved for a range and as a
function of multiple parameters. In this review, we discuss the main developments of multi …

Model Predictive Control (MPC), the dominant advanced control approach in industry over
the past twenty-five years, is presented comprehensively in this unique book. With a simple …

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 …

Model predictive control (MPC) is a very attractive candidate to replace standard field-
oriented control algorithms for electrical motors. We demonstrate that it is possible to …

Limits on the storage space or the computation time restrict the applicability of model
predictive controllers (MPC) in many real problems. Currently available methods either …

Active-set methods are recognized to often outperform other methods in terms of speed and
solution accuracy when solving small-size quadratic programming (QP) problems, making …

In this paper, we describe POP, a MATLAB toolbox for parametric optimization. It features (a)
efficient implementations of multiparametric programming problem solvers for …

During semi-autonomous driving, threat assessment is used to determine when controller
intervention that overwrites or corrects the driver's input is required. Since today's semi …

Modern industrial processes often exhibit complex and uncertain operating state fluctuations
due to the diversification of production materials, the complexity of production processes …