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Self-Triggered Model Predictive Control for Nonlinear Input-Affine Dynamical Systems via Adaptive Control Samples Selection
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-7309-8086
2017 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, no 1, 177-189 p., 7423697Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose a self-Triggered formulation of model predictive control for continuous-Time nonlinear input-Affine networked control systems. Our control method specifies not only when to execute control tasks but also provides a way to discretize the optimal control trajectory into several control samples, so that the reduction of communication load will be obtained. Stability analysis under the sample-And-hold implementation is also given, which guarantees that the state converges to a terminal region where the system can be stabilized by a local state feedback controller. Some simulation examples validate our proposed framework.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 62, no 1, 177-189 p., 7423697
Keyword [en]
Event-Triggered control, model predictive control (MPC), nonlinear systems, optimal control, Continuous time systems, Dynamical systems, Model predictive control, Networked control systems, Predictive control systems, State feedback, Adaptive Control, Communication load, Event-triggered controls, Local state feedback, Nonlinear inputs, Optimal controls, Simulation example, Stability analysis, Adaptive control systems
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-202255DOI: 10.1109/TAC.2016.2537741ISI: 000395492500031Scopus ID: 2-s2.0-85009918048OAI: oai:DiVA.org:kth-202255DiVA: diva2:1078659
Note

CODEN: IETAA. QC 20170306

Available from: 2017-03-06 Created: 2017-03-06 Last updated: 2017-11-29Bibliographically approved

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Dimarogonas, Dimos V.

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