Starting in the late 1970s, optimization-based control has built up an impressive track record
of successful industrial applications, in particular in the petrochemical and process …

This paper provides an overview of the recent research efforts on the integration of machine
learning and model predictive control under uncertainty. The paper is organized as a …

A nonlinear MPC framework is presented that is suitable for dynamical systems with
sampling times in the (sub) millisecond range and that allows for an efficient implementation …

Industrial robots have a key role in the concept of Industry 4.0. On the one hand, these
systems improve quality and productivity, but on the other hand, they require a huge amount …

Augmented Lagrangian method (ALM) has been popularly used for solving constrained
optimization problems. Practically, subproblems for updating primal variables in the …

We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex
problems with nonlinear constraints. We characterize the total computational complexity of …

We study distributed optimization where nodes cooperatively minimize the sum of their
individual, locally known, convex costs fi (x)'s; x ϵ ℝ d is global. Distributed augmented …

Over the last decades, researchers have studied the Multi-Objective Optimization (MOO)
problem for Multi-Agent Systems (MASs). However, most of them consider the problem …

We propose a new and low per-iteration complexity first-order primal-dual optimization
framework for a convex optimization template with broad applications. Our analysis relies on …

First-order methods have been studied for nonlinear constrained optimization within the
framework of the augmented Lagrangian method (ALM) or penalty method. We propose an …