Lnu-fuzzy network as a mathematical adaptive model of a hydraulic system

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F18%3A00364852" target="_blank" >RIV/68407700:21220/18:00364852 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.17973/MMSJ.2018_11_201856" target="_blank" >https://doi.org/10.17973/MMSJ.2018_11_201856</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17973/MMSJ.2018_11_201856" target="_blank" >10.17973/MMSJ.2018_11_201856</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Lnu-fuzzy network as a mathematical adaptive model of a hydraulic system

Popis výsledku v původním jazyce

Model adaptive controllers such as Model Predictive Control or Model Reference Adaptive Control need a precise mathematical model of the controlled system adaptable in real-time. Systems consisting of a hydraulic 4- way proportional valve and a linear motor have non-linear behaviour such as hysteresis of and valve, death zone of a valve spool, time delay of a data transfer and control unit, dependence on coils temperature and oil temperature and nonlinear flow characteristics. This paper introduces modified Neuro-Fuzzy network as a mathematical adaptive model of a hydraulic system with above mentioned properties. The paper presents the basic architecture of Neuro-Fuzzy network which consists of artificial neural units a fuzzy layer and introduces modifications focused on identification. The basic real-time learning method such as Normalized Gradient Descent is introduced specially for the designed Neuro-Fuzzy Network. Identification and real time learning abilities of the model were tested on the hydraulic stand.

Název v anglickém jazyce

Lnu-fuzzy network as a mathematical adaptive model of a hydraulic system

Popis výsledku anglicky

Model adaptive controllers such as Model Predictive Control or Model Reference Adaptive Control need a precise mathematical model of the controlled system adaptable in real-time. Systems consisting of a hydraulic 4- way proportional valve and a linear motor have non-linear behaviour such as hysteresis of and valve, death zone of a valve spool, time delay of a data transfer and control unit, dependence on coils temperature and oil temperature and nonlinear flow characteristics. This paper introduces modified Neuro-Fuzzy network as a mathematical adaptive model of a hydraulic system with above mentioned properties. The paper presents the basic architecture of Neuro-Fuzzy network which consists of artificial neural units a fuzzy layer and introduces modifications focused on identification. The basic real-time learning method such as Normalized Gradient Descent is introduced specially for the designed Neuro-Fuzzy Network. Identification and real time learning abilities of the model were tested on the hydraulic stand.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

MM Science Journal

ISSN

1803-1269

e-ISSN

1805-0476

Svazek periodika

2018

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

4

Strana od-do

2573-2576

Kód UT WoS článku

000532566800016

EID výsledku v databázi Scopus

2-s2.0-85057331148