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Distributed MPC Via Dual Decomposition and Alternating Direction Method of Multipliers
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
University of Melbourne, Australia.
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
2013 (English)In: Distributed Model Predictive Control Made Easy / [ed] Rudy R. Negenborn and Jose M. Maestre, Springer, 2013Chapter in book (Refereed)
Abstract [en]

A conventional way to handle model predictive control (MPC) problems distributedly is to solve them via dual decomposition and gradient ascent. However, at each time-step, it might not be feasible to wait for the dual algorithm to converge. As a result, the algorithm might be needed to be terminated prematurely. One is then interested to see if the solution at the point of termination is close to the optimal solution and when one should terminate the algorithm if a certain distance to optimality is to be guaranteed. In this chapter, we look at this problem for distributed systems under general dynamical and performance couplings, then, we make a statement on validity of similar results where the problem is solved using alternative direction method of multipliers.

Place, publisher, year, edition, pages
Springer, 2013.
Series
Intelligent Systems, Control and Automation: Science and Engineering, 69
Keyword [en]
Model Predictive Control, Distributed Optimization, Dual Decomposition, Stopping Criteria
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-111427ISBN: 978-94-007-7005-8 (print)OAI: oai:DiVA.org:kth-111427DiVA: diva2:586231
Note

QC 20131107

Available from: 2013-01-11 Created: 2013-01-11 Last updated: 2016-04-19Bibliographically approved

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Johansson, Karl Henrik

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CiteExportLink to record
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Citation style
  • apa
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  • Other locale
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Output format
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