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Event-triggered Model Predictive Control with Machine Learning for Compensation of Model Uncertainties
KTH, School of Electrical Engineering (EES). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
KTH, School of Education and Communication in Engineering Science (ECE). KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
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2017 (English)In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 5463-5468Conference paper, Published paper (Refereed)
Abstract [en]

As one of the extensions of model predictive control (MPC), event-triggered MPC takes advantage of the reduction of control updates. However, approaches to event-triggered MPCs may be subject to frequent event-triggering instants in the presence of large disturbances. Motivated by this, this paper suggests an application of machine learning to this control method in order to learn a compensation model for disturbance attenuation. The suggested method improves both event-triggering policy efficiency and control accuracy compared to previous approaches to event-triggered MPCs. We employ the radial basis function (RBF) kernel based machine learning technique. By the universial approximation property of the RBF, which imposes an upper bound on the training error, we can present the stability analysis of the learningaided control system. The proposed algorithm is evaluated by means of position control of a nonholonomic robot subject to state-dependent disturbances. Simulation results show that the developed method yields not only two times less event triggering instants, but also improved tracking performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 5463-5468
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-223877DOI: 10.1109/CDC.2017.8264468ISI: 000424696905039Scopus ID: 2-s2.0-85046154893ISBN: 978-1-5090-2873-3 OAI: oai:DiVA.org:kth-223877DiVA, id: diva2:1187565
Conference
IEEE 56th Annual Conference on Decision and Control (CDC), DEC 12-15, 2017, Melbourne, Australia
Funder
Knut and Alice Wallenberg FoundationSwedish Research CouncilSwedish Foundation for Strategic Research
Note

QC 20180305

Available from: 2018-03-05 Created: 2018-03-05 Last updated: 2018-06-04Bibliographically approved

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Yoo, JaehyunJafarian, MatinDimarogonas, Dimos V.Johansson, Karl H.

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Yoo, JaehyunJafarian, MatinDimarogonas, Dimos V.Johansson, Karl H.
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School of Electrical Engineering (EES)ACCESS Linnaeus CentreSchool of Education and Communication in Engineering Science (ECE)ACCESS Linnaeus Centre
Electrical Engineering, Electronic Engineering, Information Engineering

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