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 …

Starting in the late 1970s, optimization-based control has built up an impressive track record
of successful industrial applications, in particular in the petrochemical and process …

The increasing heterogeneity and scalability of the Internet of everything, especially, the
Energy Internet (EI), is a prompt for novel engineering paradigms. The current infrastructure …

In this paper we present the main foundations and features of an integrated framework and
software platform that enables the use of model-based tools in design, operational …

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

In hybrid model predictive control (MPC), a mixed-integer quadratic program (MIQP) is
solved at each sampling time to compute the optimal control action. Although these …

The inevitable presence of uncertain parameters in critical applications of process
optimization can lead to undesirable or infeasible solutions. For this reason, optimization …

The ability to solve model predictive control (MPC) problems of linear time-invariant systems
explicitly and offline via multi-parametric quadratic programming (mp-QP) has become a …

This article presents an algorithm for the exact solution of explicit hybrid model-predictive
control problems of time-invariant, discrete-time mixed logical dynamical systems. Using …

In this paper, an explicit nonlinear model predictive controller (ENMPC) for the stabilization
of nonlinear systems is investigated. The proposed ENMPC is constructed using tensored …