relation: http://eprints.imtlucca.it/731/
title: Decentralized model predictive control 
creator: Bemporad, Alberto
creator: Barcelli, Davide
subject: TJ Mechanical engineering and machinery
description: Decentralized and distributed model predictive control (DMPC) addresses the problem of controlling a multivariable dynamical process, composed by several interacting subsystems and subject to constraints, in a computation and communication efficient way. Compared to a centralized MPC setup, where a global optimal control problem must be solved on-line with respect to all actuator commands given the entire set of states, in DMPC the control problem is divided into a set of local MPCs of smaller size, that cooperate by communicating each other a certain information set, such as local state measurements, local decisions, optimal local predictions. Each controller is based on a partial (local) model of the overall dynamics, possibly neglecting existing dynamical interactions. The global performance objective is suitably mapped into a local objective for each of the local MPC problems.  This chapter surveys some of the main contributions to DMPC, with an emphasis on a method developed by the authors, by illustrating the ideas on motivating examples. Some novel ideas to address the problem of hierarchical MPC design are also included in the chapter.  
publisher: Springer-Verlag
contributor: Bemporad, Alberto
contributor: Heemels, W.P.M.H.
contributor: Johansson, Mikael
date: 2010
type: Book Section
type: PeerReviewed
identifier:   Bemporad, Alberto and Barcelli, Davide  Decentralized model predictive control.   In:  Networked control systems.   Lecture Notes in Control and Information Sciences, 406 .  Springer-Verlag, pp. 149-178.  ISBN 978-0-85729-032-8      (2010)  
relation: http://dx.doi.org/10.1007/978-0-85729-033-5_5
relation: 10.1007/978-0-85729-033-5_5