Distributed nonlinear model predictive control through accelerated parallel ADMM

Wentao Tang, Prodromos Daoutidis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Alternating direction method of multipliers (ADMM), as a powerful distributed optimization algorithm, provides a framework for distributed model predictive control (MPC) for nonlinear process systems based on local subsystem model information. However, the practical application of classical ADMM is largely limited by the high computational cost caused by its slow (linear) rate of convergence and non-parallelizability. In this work, we combine a recently developed multi-block parallel ADMM algorithm with a Nesterov acceleration technique into a fast ADMM scheme, and apply it to the solution of optimal control problems associated with distributed nonlinear MPC. A benchmark chemical process is considered for a case study, which demonstrates a significant reduction of computational time and communication effort compared to non-parallel and non-accelerated ADMM counterparts.

Original languageEnglish (US)
Title of host publication2019 American Control Conference, ACC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1406-1411
Number of pages6
ISBN (Electronic)9781538679265
DOIs
StatePublished - Jul 2019
Event2019 American Control Conference, ACC 2019 - Philadelphia, United States
Duration: Jul 10 2019Jul 12 2019

Publication series

NameProceedings of the American Control Conference
Volume2019-July
ISSN (Print)0743-1619

Conference

Conference2019 American Control Conference, ACC 2019
Country/TerritoryUnited States
CityPhiladelphia
Period7/10/197/12/19

Bibliographical note

Funding Information:
*Financial support from the National Science Foundation (NSF-CBET) is gratefully acknowledged.

Publisher Copyright:
© 2019 American Automatic Control Council.

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