Instant Distributed Model Predictive Control for Constrained Linear Systems

Martin Figura, Lanlan Su, Vijay Gupta, Masaki Inoue

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

Abstract

Distributed optimal control has emerged as an exciting possibility; however, existing algorithms tend to require excessive computational time and thus may not be able to stabilize systems with fast dynamics. We develop instant distributed model predictive control (iDMPC) with a realization of the primal-dual algorithm embedded in the controller dynamics. Under assumptions on fast communication, we show that the input and state trajectories of iDMPC are equivalent to a centralized suboptimal MPC scheme. We utilize a dissipativity analysis to show that the closed-loop system trajectories asymptotically converge to a desired reference.

Original languageEnglish
Title of host publication2020 American Control Conference, ACC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4582-4587
Number of pages6
ISBN (Electronic)9781538682661
DOIs
Publication statusPublished - 2020 Jul
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: 2020 Jul 12020 Jul 3

Publication series

NameProceedings of the American Control Conference
Volume2020-July
ISSN (Print)0743-1619

Conference

Conference2020 American Control Conference, ACC 2020
CountryUnited States
CityDenver
Period20/7/120/7/3

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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  • Cite this

    Figura, M., Su, L., Gupta, V., & Inoue, M. (2020). Instant Distributed Model Predictive Control for Constrained Linear Systems. In 2020 American Control Conference, ACC 2020 (pp. 4582-4587). [9147976] (Proceedings of the American Control Conference; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC45564.2020.9147976