This article provides an overview of model predictive control (MPC) frameworks for dynamic
operation of nonlinear constrained systems. Dynamic operation is often an integral part of …

Solving the power flow problem in a distributed fashion empowers different grid operators to
compute the overall grid state without having to share grid models—this is a practical …

This paper consists of a tutorial on the Augmented Lagrangian based Alternating Direction
Inexact Newton method (ALADIN) and its application to distributed model predictive control …

This article presents a distributed data-driven predictive control approach using the
behavioral framework. It aims to design a network of controllers for an interconnected system …

The modular open-source framework GRAMPC-D for model predictive control of distributed
systems is presented in this paper. The modular concept allows to solve optimal control …

Abstract Model Predictive Control (MPC) for {networked, cyber-physical, multi-agent}
systems requires numerical methods to solve optimal control problems while meeting …

A coupled dynamic-static decentralized robust state estimation (RSE) methodology is
proposed for integrated electricity-gas systems (IEGSs). Firstly, a discrete time state space …

This paper introduces a new method for solving the distributed ac power flow (pf) problem by
further exploiting the problem formulation. We propose a new variant of the aladin algorithm …

This paper proposes an iterative distributed model predictive control (DMPC) algorithm for
multiple dynamically decoupling linear systems subject to both local state and input …

This paper presents an asynchronous formulation of the alternating direction method of
multipliers (ADMM) for solving optimal control problems that arise in distributed model …