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Distributed nonlinear model predictive control based on contraction theory
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2018 (English)In: International Journal of Robust and Nonlinear Control, ISSN 1049-8923, E-ISSN 1099-1239, Vol. 28, no 2, p. 492-503Article in journal (Refereed) Published
Abstract [en]

A novel distributed model predictive control algorithm for continuous-time nonlinear systems is proposed in this paper. Contraction theory is used to estimate the prediction error in the algorithm, leading to new feasibility and stability conditions. Compared to existing analysis based on Lipschitz continuity, the proposed approach gives a distributed model predictive control algorithm under less conservative conditions, allowing stronger couplings between subsystems and a larger sampling interval when the subsystems satisfy the specified contraction conditions. A numerical example is given to illustrate the effectiveness and advantage of the proposed approach.

Place, publisher, year, edition, pages
WILEY , 2018. Vol. 28, no 2, p. 492-503
Keywords [en]
contraction theory, distributed control, model predictive control, nonlinear systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-220808DOI: 10.1002/rnc.3881ISI: 000418409100007Scopus ID: 2-s2.0-85025100563OAI: oai:DiVA.org:kth-220808DiVA, id: diva2:1174755
Note

QC 20170116

Available from: 2018-01-16 Created: 2018-01-16 Last updated: 2018-01-16Bibliographically approved

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