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A set-based model-free reinforcement learning design technique for nonlinear systems
Department of Chemical Engineering, Queen's University, Kingston, ON, Canada.ORCID iD: 0000-0003-4660-7978
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-9901-5776
2019 (English)In: International Journal of Adaptive Control and Signal Processing, ISSN 1099-1115, Vol. 33, no 2, p. 315-334Article in journal (Refereed) Published
Abstract [en]

Summary In this study, we propose an extremum-seeking approach for the approximation of optimal control problems for a class of unknown nonlinear dynamical systems. The technique combines a phasor extremum-seeking controller with a reinforcement learning strategy. The learning approach is used to estimate the value function of an optimal control problem of interest. The phasor extremum-seeking controller implements the approximate optimal controller. The approach is shown to provide reasonable approximations of optimal control problems without the need for a parameterization of the nonlinear system’s dynamics. A simulation example is provided to demonstrate the effectiveness of the technique.

Place, publisher, year, edition, pages
John Wiley & Sons, 2019. Vol. 33, no 2, p. 315-334
Keywords [en]
extremum-seeking control, phasor approximation, reinforcement learning
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-71744DOI: 10.1002/acs.2949ISI: 000458861100006Scopus ID: 2-s2.0-85058854004OAI: oai:DiVA.org:ltu-71744DiVA, id: diva2:1266409
Note

Validerad;2019;Nivå 2;2019-02-06 (johcin)

Available from: 2018-11-28 Created: 2018-11-28 Last updated: 2019-03-14Bibliographically approved

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Atta, Khalid

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CiteExportLink to record
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  • apa
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  • de-DE
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  • nn-NB
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  • Other locale
More languages
Output format
  • html
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  • asciidoc
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