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)Full text not available from this repository.
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|
|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|
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