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Decentralized model predictive control

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)

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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.

Item Type: Book Section
Identification Number: 10.1007/978-0-85729-033-5_5
Subjects: T Technology > TJ Mechanical engineering and machinery
Research Area: Computer Science and Applications
Depositing User: Professor Alberto Bemporad
Date Deposited: 28 Jul 2011 10:49
Last Modified: 02 Jul 2014 14:14
URI: http://eprints.imtlucca.it/id/eprint/731

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