Neuro-Evolution of Continuous-Time Dynamic Process Controllers
dc.contributor.author | Sekaj, Ivan | |
dc.contributor.author | Kénický, Ivan | |
dc.contributor.author | Zúbek, Filip | |
dc.coverage.issue | 2 | cs |
dc.coverage.volume | 27 | cs |
dc.date.accessioned | 2022-01-26T08:21:42Z | |
dc.date.available | 2022-01-26T08:21:42Z | |
dc.date.issued | 2021-12-21 | cs |
dc.description.abstract | Artificial 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.format | text | cs |
dc.format.extent | 7-11 | cs |
dc.format.mimetype | application/pdf | en |
dc.identifier.citation | Mendel. 2021 vol. 27, č. 2, s. 7-11. ISSN 1803-3814 | cs |
dc.identifier.doi | 10.13164/mendel.2021.2.007 | en |
dc.identifier.issn | 2571-3701 | |
dc.identifier.issn | 1803-3814 | |
dc.identifier.uri | http://hdl.handle.net/11012/203386 | |
dc.language.iso | en | cs |
dc.publisher | Institute of Automation and Computer Science, Brno University of Technology | cs |
dc.relation.ispartof | Mendel | cs |
dc.relation.uri | https://mendel-journal.org/index.php/mendel/article/view/153 | cs |
dc.rights | Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license | en |
dc.rights.access | openAccess | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0 | en |
dc.subject | Continuous-Time Controller | en |
dc.subject | Non-linear Dynamic System | en |
dc.subject | Artificial Neural Network | en |
dc.subject | Genetic Algorithm-Based Learning | en |
dc.subject | Control Performance | en |
dc.title | Neuro-Evolution of Continuous-Time Dynamic Process Controllers | en |
dc.type.driver | article | en |
dc.type.status | Peer-reviewed | en |
dc.type.version | publishedVersion | en |
eprints.affiliatedInstitution.faculty | Fakulta strojního inženýrství | cs |
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