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Erschienen in:
The Essentials of Model Predictive Control Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

Distributed MPC for Large-Scale Systems

verfasst von : Marcello Farina, Riccardo Scattolini

Erschienen in: Handbook of Model Predictive Control

Verlag: Springer International Publishing

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Abstract

This chapter presents the main approaches to the design of Distributed Model Predictive Control (DMPC) algorithms. For simplicity, focus is placed on the control of linear, time-invariant, discrete time systems. Emphasis is initially given to the system and problem partitioning, and a discussion is reported on how the adopted decomposition strongly influences the properties of the control scheme in terms of minimality of the representation, descriptive capabilities of the model, and information transmission requirements. Then, after a short discussion on decentralized MPC, a taxonomy of DMPC methods is proposed and some prototype DMPC algorithms are described with the aim to highlight the main characteristics of the classes of methods nowadays available. In the final part of the chapter, the most promising directions of research in the field are briefly summarized, and the most interesting fields of application of DMPC are listed.

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Vorheriges Kapitel Model Predictive Control of Polynomial Systems
Nächstes Kapitel Scalable MPC Design
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Metadaten
Titel
Distributed MPC for Large-Scale Systems
verfasst von
Marcello Farina
Riccardo Scattolini
Copyright-Jahr
2019
Buch
Handbook of Model Predictive Control

Print ISBN: 978-3-319-77488-6

Electronic ISBN: 978-3-319-77489-3

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-77489-3_11

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