Neuro-Evolution of Continuous-Time Dynamic Process Controllers

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dc.contributor.authorSekaj, Ivan
dc.contributor.authorKénický, Ivan
dc.contributor.authorZúbek, Filip
dc.coverage.issue2cs
dc.coverage.volume27cs
dc.date.accessioned2022-01-26T08:21:42Z
dc.date.available2022-01-26T08:21:42Z
dc.date.issued2021-12-21cs
dc.description.abstractArtificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised learning methods can be used. But in case of controller design of dynamic systems the required (optimal) controller output is a-priori unknown, supervised learning cannot be used. In such case we only can define some criterion function, which represents the required control performance of the closed-loop system. We present a neuro-evolution design for control of a continuous-time controller of non-linear dynamic systems. The controller is represented by an MLP-type artificial neural network. The learning algorithm of the neural network is based on an evolutionary approach with genetic algorithm. An integral-type performance index representing control quality, which is based on closed-loop simulation, is minimised. The results are demonstrated on selected experiments with controller reference value changes as well as with noisy system outputs.en
dc.formattextcs
dc.format.extent7-11cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationMendel. 2021 vol. 27, č. 2, s. 7-11. ISSN 1803-3814cs
dc.identifier.doi10.13164/mendel.2021.2.007en
dc.identifier.issn2571-3701
dc.identifier.issn1803-3814
dc.identifier.urihttp://hdl.handle.net/11012/203386
dc.language.isoencs
dc.publisherInstitute of Automation and Computer Science, Brno University of Technologycs
dc.relation.ispartofMendelcs
dc.relation.urihttps://mendel-journal.org/index.php/mendel/article/view/153cs
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0en
dc.subjectContinuous-Time Controlleren
dc.subjectNon-linear Dynamic Systemen
dc.subjectArtificial Neural Networken
dc.subjectGenetic Algorithm-Based Learningen
dc.subjectControl Performanceen
dc.titleNeuro-Evolution of Continuous-Time Dynamic Process Controllersen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta strojního inženýrstvícs
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Vol. 27, No. 2