Fuzzy Control of a Helio-Crane

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00210102" target="_blank" >RIV/68407700:21230/13:00210102 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s10846-012-9796-0" target="_blank" >http://dx.doi.org/10.1007/s10846-012-9796-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10846-012-9796-0" target="_blank" >10.1007/s10846-012-9796-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy Control of a Helio-Crane

Popis výsledku v původním jazyce

In this paper we present a comparison of two fuzzy-control approaches that were developed for use on a non-linear single-input single-output (SISO) system. The first method is Fuzzy Model Reference Learning Control (FMRLC) with a modified adaptation mechanism that tunes the fuzzy inverse model. The basic idea of this method is based on shifting the output membership functions in the fuzzy controller and in the fuzzy inverse model. The second approach is a 2 degrees-of-freedom (2 DOF) control that is based on the Takagi-Sugeno fuzzy model. The T-S fuzzy model is obtained by identification of evolving fuzzy model and then used in the feed-forward and feedback parts of the controller. An error-model predictive-control approach is used for the design of the feedback loop. The controllers were compared on a non-linear second-order SISO system named the helio-crane. We compared the performance of the reference tracking in a simulation environment and on a real system. Both methods provided a

Název v anglickém jazyce

Fuzzy Control of a Helio-Crane

Popis výsledku anglicky

In this paper we present a comparison of two fuzzy-control approaches that were developed for use on a non-linear single-input single-output (SISO) system. The first method is Fuzzy Model Reference Learning Control (FMRLC) with a modified adaptation mechanism that tunes the fuzzy inverse model. The basic idea of this method is based on shifting the output membership functions in the fuzzy controller and in the fuzzy inverse model. The second approach is a 2 degrees-of-freedom (2 DOF) control that is based on the Takagi-Sugeno fuzzy model. The T-S fuzzy model is obtained by identification of evolving fuzzy model and then used in the feed-forward and feedback parts of the controller. An error-model predictive-control approach is used for the design of the feedback loop. The controllers were compared on a non-linear second-order SISO system named the helio-crane. We compared the performance of the reference tracking in a simulation environment and on a real system. Both methods provided a

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GAP103%2F12%2F1187" target="_blank" >GAP103/12/1187: Identifikace stochastických, nelineárních systémů pro pokročilé řízení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Journal of Intelligent and Robotic Systems

ISSN

0921-0296

e-ISSN

—

Svazek periodika

72

Číslo periodika v rámci svazku

3-4

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

19

Strana od-do

497-515

Kód UT WoS článku

000326192300014

EID výsledku v databázi Scopus

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