This paper presents a new methodology for selecting control structure in the context of
distributed model predictive control. An index was developed to quantify the performance of …

Although distributed model predictive control (DMPC) has received significant attention in
the literature, the robustness of DMPC with respect to model errors has not been explicitly …

A distributed model predictive control (DMPC) strategy brings interesting features of
topology, flexibility and maintenance to large-scale nonlinear systems. This work presents …

Abstract In this paper, a coordinated-distributed model predictive control (MPC) scheme is
presented for large-scale discrete-time linear process systems. Coordinated-distributed …

This study presents a review of distributed model predictive control (DMPC) strategies
followed by recent studies conducted by the authors on the robustness of these strategies to …

A distributed model predictive control (DMPC) framework is proposed. The physical plant
structure and the plant mathematical model are used to partition the system into self …

In this work, we focus on distributed model predictive control (DMPC) of large scale
nonlinear process systems in which several distinct sets of manipulated inputs are used to …

In this paper, a partitioning approach for large-scale systems based on graph-theory is
presented. The algorithm starts with the translation of the system model into a graph …

Recently, there has been a renewed interest in the development of distributed model
predictive control (MPC) techniques capable of inheriting the properties of centralized …

This paper investigates the impact of control architecture design on the distributed model
predictive control (MPC) of nonlinear complex process networks. A sequential distributed …