This paper recalls a few past achievements in Model Predictive Control, gives an overview
of some current developments and suggests a few avenues for future research.

CONTROLLER design is more troublesome in aerospace systems due to, inter alia, diversity
of mission platforms, convoluted nonlinear dynamics, predominantly strict mission and …

Today's control engineering problems exhibit an unprecedented complexity, with examples
including the reliable integration of renewable energy sources into power grids, safe …

A supervised learning framework is proposed to approximate a model predictive controller
(MPC) with reduced computational complexity and guarantees on stability and constraint …

The promise of collecting and utilizing large amounts of data has never been greater in the
history of urban water management (UWM). This paper reviews several data-driven …

The control problem was formulated in the previous chapters considering all signals to
possess an unlimited range. This is not very realistic because in practice all processes are …

In this article, we present a nonlinear robust model predictive control (MPC) framework for
general (state and input dependent) disturbances. This approach uses an online …

The aim of this paper is to review and to propose a classification of a number of
decentralized, distributed and hierarchical control architectures for large scale systems …

This paper is concerned with the optimal control of linear discrete-time systems subject to
unknown but bounded state disturbances and mixed polytopic constraints on the state and …

Two robust model predictive control (MPC) schemes are proposed for tracking unicycle
robots with input constraint and bounded disturbances: tube-MPC and nominal robust MPC …