In this paper, we discuss the model predictive control algorithms that are tailored for
uncertain systems. Robustness notions with respect to both deterministic (or set based) and …

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 …

This monograph provides insight and fundamental understanding into the feedback control
of nonlinear and hybrid process systems. It presents state-of-the-art methods for the …

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 …

We propose a global optimisation approach for the solution of various classes of bilevel
programming problems (BLPP) based on recently developed parametric programming …

This work considers the problem of stabilization of nonlinear systems subject to constraints,
uncertainty and faults in the control actuator. We first design a robust model predictive …

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

Achieving high performance process control (HPPC) requires that the control system
operate the plant at optimal efficiency over the full range of steady state and dynamic …

In this work, we consider nonlinear systems with input constraints and uncertain variables,
and develop a robust hybrid predictive control structure that provides a safety net for the …