Motivated by requirements in the process industries, the largest user of model predictive
control, we re-examine some features of recent research on this topic. We suggest that some …

Abstract Stochastic Model Predictive Control (SMPC) accounts for model uncertainties and
disturbances based on their probabilistic description. This chapter considers several …

In this article, we present a novel linear model predictive control (MPC) strategy for
controlling uncertain dynamical systems. The moment based MPC strategy is based on the …

Perception-aware Chance-constrained Model Predictive Control (PAC-MPC) accounts for
the interdependence between perception and control for systems operating in uncertain …

We consider a model predictive control setting, where we use the alternating direction
method of multipliers (ADMM) to exploit problem structure. We take advantage of interacting …

We present an open-source solution for the operational control of drinking water distribution
networks which accounts for the inherent uncertainty in water demand and electricity prices …

In a range of applications, model predictive control (MPC) is implemented on embedded
devices, motivating the use of conceptually and computationally simple optimization …

In this thesis, data driven approaches to sequential decision making problems within
process systems engineering (PSE) are developed. Specifically, the use of model-free …

This thesis contributes towards the development of a fast optimal control algorithm, relying
on the Alternating-Direction of Multipliers (ADMM), for solving large-scale linear convex multi …

Control of nonlinear uncertain systems is a common challenge in the robotics field.
Nonlinear latent force models, which incorporate latent uncertainty characterized as …