It has been proven that advanced building control, like model predictive control (MPC), can
notably reduce the energy use and mitigate greenhouse gas emissions. However, despite …

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 …

Challenges related to automated driving are no longer focused on just the construction of
such automated vehicles (AVs) but also on assuring the safety of operation. Recent …

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

The full deployment of autonomous driving systems on a worldwide scale requires that the
self-driving vehicle can be operated in a provably safe manner, ie, the vehicle must be able …

This paper presents a self-triggered MPC controller design strategy for linear systems with
state and input constraints. Based on the so-called relaxed dynamic programming …

We propose a scalable method for forward stochastic reachability analysis for uncontrolled
linear systems with affine disturbance. Our method uses Fourier transforms to efficiently …

Connected and autonomous vehicles (CAVs) are expected to operate with safety guarantee
in presence of adversaries from the Internet of Vehicles. This article proposes a control …

This paper proposes a novel MPC approach called conditional scenario-based model
predictive control (CSB-MPC), developed for discrete-time linear systems affected by …

Tunability is a major obstacle in the creation and subsequent application of the explicit
model predictive control (MPC). The main bottleneck lies in the need to reconstruct the …