Inverse Model Approximation Using Iterative Method and Neural Networks with Practical Application for Unstable Nonlinear System Control

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F19%3APU130441" target="_blank" >RIV/00216305:26210/19:PU130441 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Inverse Model Approximation Using Iterative Method and Neural Networks with Practical Application for Unstable Nonlinear System Control

Popis výsledku v původním jazyce

In this paper a method for controlling and stabilizing an unstable nonlinear system using a NARX neural network is presented. It is difficult to design a common feedback controller or even perform system identification on unstable systems, more even so if these systems are nonlinear. To compensate for nonlinearity a feedforward controller is required. In this paper we present a method of estimating inverse model of the system for the feedforward controller using a NARX artificial neural network in an iterative approach which takes less time than methods commonly used and performs as good. This method is verified and tested on an educational model of magnetic levitation of steel ball. Both static and dynamic forms of the inverse model are presented and evaluated with positive results.

Název v anglickém jazyce

Inverse Model Approximation Using Iterative Method and Neural Networks with Practical Application for Unstable Nonlinear System Control

Popis výsledku anglicky

In this paper a method for controlling and stabilizing an unstable nonlinear system using a NARX neural network is presented. It is difficult to design a common feedback controller or even perform system identification on unstable systems, more even so if these systems are nonlinear. To compensate for nonlinearity a feedforward controller is required. In this paper we present a method of estimating inverse model of the system for the feedforward controller using a NARX artificial neural network in an iterative approach which takes less time than methods commonly used and performs as good. This method is verified and tested on an educational model of magnetic levitation of steel ball. Both static and dynamic forms of the inverse model are presented and evaluated with positive results.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

PROCEEDINGS OF THE 2018 18TH INTERNATIONAL CONFERENCE ON MECHATRONICS - MECHATRONIKA (ME)

ISBN

978-80-214-5542-9

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

209-215

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Brno

Datum konání akce

5. 12. 2018

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

000465104200033