Over the past decades, the advantages of optimization-based control techniques over
conventional controllers inspired developments that enabled the use of model predictive …

This paper presents a method to compute an approximate explicit model predictive control
(MPC) law using neural networks. The optimal MPC control law for constrained linear …

In this original book on model predictive control (MPC) for power electronics, the focus is put
on high-power applications with multilevel converters operating at switching frequencies …

This dissertation explores the building blocks needed to efficiently formulate and solve
optimal control problems. The premise of the thesis is that existing general-purpose solvers …

Receding horizon control requires the solution of an optimization problem at every sampling
instant. We present efficient interior point methods tailored to convex multistage problems, a …

This paper proposes to use Nesterov's fast gradient method for the solution of linear
quadratic model predictive control (MPC) problems with input constraints. The main focus is …

This paper proposes a dual fast gradient-projection method for solving quadratic
programming problems that arise in model predictive control of linear systems subject to …

We propose a distributed optimization algorithm for mixed L1/L2-norm optimization based
on accelerated gradient methods using dual decomposition. The algorithm achieves …

We apply an operator splitting technique to a generic linear-convex optimal control problem,
which results in an algorithm that alternates between solving a quadratic control problem, for …

In this article, we propose a novel framework for approximating the MPC policy for linear
parameter-varying systems using supervised learning. Our learning scheme guarantees …