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.Abstract
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: | https://doi.org/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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