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Title:Samosprožilno mrežno vodenje z nelinearnim modelom na osnovi globokega učenja : magistrsko delo
Authors:ID Vogrinčič, Sebastjan (Author)
ID Sarjaš, Andrej (Mentor) More about this mentor... New window
Files:.pdf MAG_Vogrincic_Sebastjan_2023.pdf (4,37 MB)
MD5: 639EF1AE5C962455EC25AF8FEA00B610
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrska naloga opisuje modeliranje nelinearnih dinamičnih sistemov in implementacijo samosprožilnega dogodkovnega vodenja na sistemu zračne levitacije z namenom reševanja sodobnih problemov vodenja, kot je preobremenjenost omrežja. Najprej smo vzpostavili komunikacijo med sistemom in računalnikom z namenom priprave podatkov. Sledila je faza globokega učenja in validacija modela. Na koncu smo načrtali ustrezen algoritem, ki posodablja izhod regulatorja glede na predikcijo modela. Z magistrskim delom smo predvsem dokazali delovanje obravnavanega vodenja na hitrem nelinearnem in nestabilnem sistemu. Ugotovili smo, da je zanesljivost takega vodenja predvsem odvisna od natančnosti modela. Samosprožilno vodenje je lahko riskantno, zato je za industrijsko aplikacijo potrebno vpeljati dodatne varnostne mehanizme.
Keywords:samosprožilno vodenje, dogodkovno proženje, nelinearni model, globoko učenje, NARX.
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[S. Vogrinčič]
Year of publishing:2023
Number of pages:1 spletni vir (1 datoteka PDF (XII, 61 f.))
PID:20.500.12556/DKUM-84946 New window
UDC:681.521:004.85(043.2)
COBISS.SI-ID:170018563 New window
Publication date in DKUM:05.10.2023
Views:424
Downloads:76
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:10.08.2023

Secondary language

Language:English
Title:Self-triggered network control with a nonlinear model based on deep learning
Abstract:The master's thesis describes the modelling of nonlinear dynamical systems and the implementation of a self-triggered control on the air levitation system with the aim of solving modern control problems such as network overload. First, we established communication between the system and the computer for the purpose of data preparation. This is followed by the phase of deep learning and model validation. Finally, we designed an appropriate algorithm that updates the controller output according to the model prediction. With the master's thesis, we mainly proved the operation of the discussed control on a fast nonlinear and unstable system. We found that the reliability of this control depends mainly on the accuracy of the model. Self-triggered control can be risky and therefore it is necessary to introduce additional safety mechanisms for an industrial application.
Keywords:self-triggered control, event based sampling, nonlinear model, deep learning, NARX.


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