This paper presents a dual decomposition-based distributed optimization algorithm and
applies it to distributed model predictive control (DMPC) problems. The considered DMPC …

In distributed model predictive control (DMPC), where a centralized optimization problem is
solved in distributed fashion using dual decomposition, it is important to keep the number of …

This paper proposes a DMPC approach for a family of discrete-time linear systems with local
(uncoupled) and global (coupled) constraints. The proposed approach is based on the dual …

This paper presents a systematic computational study on the performance of distributed
optimization in model predictive control (MPC). We consider networks of dynamically …

The paper presents an effective coordination scheme for a distributed optimization based on
dual decomposition. The targeted class of optimization problems are strictly convex …

In this paper we present a dual-based decomposition method, called here the proximal
center method, to solve distributed model predictive control (MPC) problems for coupled …

In this paper we introduce an iterative distributed Jacobi algorithm for solving convex
optimization problems, which is motivated by distributed model predictive control (MPC) for …

Dual decomposition is an efficient tool in dealing with Model Predictive Control (MPC)
problems, particularly for distributed MPC. In this paper, we propose to limit the iteration …

In this paper, we propose to reduce the number of iterations required in the implementation
of distributed Model Predictive Control (dMPC) based on dual decomposition. To this aim, a …

A conventional way to handle model predictive control (MPC) problems distributedly is to
solve them via dual decomposition and gradient ascent. However, at each time-step, it might …