%0 Book Section
%A Bemporad, Alberto
%A Barcelli, Davide
%B Networked control systems
%D 2010
%E Bemporad, Alberto
%E Heemels, W.P.M.H.
%E Johansson, Mikael
%F eprints:731
%I Springer-Verlag
%P 149-178
%S Lecture Notes in Control and Information Sciences
%T Decentralized model predictive control 
%U http://eprints.imtlucca.it/731/
%V 406
%X 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.