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Stochastic self-triggered model predictive control for linear systems with probabilistic constraints
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
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2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 92, p. 9-17Article in journal (Refereed) Published
Abstract [en]

A stochastic self-triggered model predictive control (SSMPC) algorithm is proposed for linear systems subject to exogenous disturbances and probabilistic constraints. The main idea behind the self-triggered framework is that at each sampling instant, an optimization problem is solved to determine both the next sampling instant and the control inputs to be applied between the two sampling instants. Although the self-triggered implementation achieves communication reduction, the control commands are necessarily applied in open-loop between sampling instants. To guarantee probabilistic constraint satisfaction, necessary and sufficient conditions are derived on the nominal systems by using the information on the distribution of the disturbances explicitly. Moreover, based on a tailored terminal set, a multi-step open-loop MPC optimization problem with infinite prediction horizon is transformed into a tractable quadratic programming problem with guaranteed recursive feasibility. The closed-loop system is shown to be stable. Numerical examples illustrate the efficacy of the proposed scheme in terms of performance, constraint satisfaction, and reduction of both control updates and communications with a conventional time-triggered scheme.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 92, p. 9-17
Keyword [en]
Model predictive control (MPC), Probabilistic constraints, Self-triggered control, Stochastic systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-227558DOI: 10.1016/j.automatica.2018.02.017ISI: 000431159300002Scopus ID: 2-s2.0-85045943840OAI: oai:DiVA.org:kth-227558DiVA, id: diva2:1204907
Funder
Swedish Research Council, 61633014Swedish Foundation for Strategic Research Knut and Alice Wallenberg Foundation
Note

QC 20180509

Available from: 2018-05-09 Created: 2018-05-09 Last updated: 2018-05-15Bibliographically approved

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Gao, Yulong

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