Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms

This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomecha...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent & robotic systems 2003-10, Vol.38 (2), p.213-235
Main Authors: Lin, Chorng-shyan, Yang, Tachung, Jou, Yeong-chau, Lin, Lih-chang
Format: Article
Language:English
Subjects:
Artificial neural networks
Construction
Dynamical systems
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Nonlinear dynamics
Servomechanisms
Studies
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 235
container_issue 2
container_start_page 213
container_title Journal of intelligent & robotic systems
container_volume 38
creator Lin, Chorng-shyan
Yang, Tachung
Jou, Yeong-chau
Lin, Lih-chang
description This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomechanism including the effects of flexibility and friction. The RLS (recursive least squares) algorithm is adopted for obtaining the optimal consequent parameters of the rules. Within each fuzzy operating region, a local MDPP (minimum degree pole placement) control law with integral action can be constructed based on the estimated local model. Then a fuzzy controller composed of these local MDPP controls can be easily constructed for the servomechanism. The techniques are illustrated using computer simulations.
doi_str_mv 10.1023/A:1027339220324
format article
fullrecord <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_miscellaneous_914646557</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>914646557</sourcerecordid><originalsourceid>FETCH-LOGICAL-p243t-327713f9a5e91bba77439e4f6128a4a3faffc991d2a0394d0dcd17905dcbef773</originalsourceid><addsrcrecordid>eNp9kMFLwzAYxYMoOKtnr8GDnqpJvqTp521Mp8LUMfU80jbRjq6ZSSu6v96JO3nw9IPHjwfvEXLM2TlnAi6GlxtoABSCgZA7ZMCVhpRJhrtkwFDwlAnM9slBjAvGGOYKB2Q2s2Ufgm07-mD74NNxv15_0Xtf2aZuX6lpK7qNrqZTOvJtF3xDnQ903NjPumgsfbLhwy9t-WbaOi7jIdlzpon2aMuEvIyvn0e36eTx5m40nKQrIaFLQWjNwaFRFnlRGK0loJUu4yI30oAzzpWIvBKGAcqKVWXFNTJVlYV1WkNCzn57V8G_9zZ282UdS9s0prW-j3PkMpOZUj_m6b-m0AiZ2JyYkJM_4sL3od2smOc5zzQolcM3tu9sjQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>881673558</pqid></control><display><type>article</type><title>Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms</title><source>Springer Link</source><creator>Lin, Chorng-shyan ; Yang, Tachung ; Jou, Yeong-chau ; Lin, Lih-chang</creator><creatorcontrib>Lin, Chorng-shyan ; Yang, Tachung ; Jou, Yeong-chau ; Lin, Lih-chang</creatorcontrib><description>This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomechanism including the effects of flexibility and friction. The RLS (recursive least squares) algorithm is adopted for obtaining the optimal consequent parameters of the rules. Within each fuzzy operating region, a local MDPP (minimum degree pole placement) control law with integral action can be constructed based on the estimated local model. Then a fuzzy controller composed of these local MDPP controls can be easily constructed for the servomechanism. The techniques are illustrated using computer simulations.</description><identifier>ISSN: 0921-0296</identifier><identifier>EISSN: 1573-0409</identifier><identifier>DOI: 10.1023/A:1027339220324</identifier><language>eng</language><publisher>Dordrecht: Springer Nature B.V</publisher><subject>Artificial neural networks ; Construction ; Dynamical systems ; Fuzzy ; Fuzzy logic ; Fuzzy set theory ; Fuzzy sets ; Nonlinear dynamics ; Servomechanisms ; Studies</subject><ispartof>Journal of intelligent &amp; robotic systems, 2003-10, Vol.38 (2), p.213-235</ispartof><rights>Kluwer Academic Publishers 2003</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Lin, Chorng-shyan</creatorcontrib><creatorcontrib>Yang, Tachung</creatorcontrib><creatorcontrib>Jou, Yeong-chau</creatorcontrib><creatorcontrib>Lin, Lih-chang</creatorcontrib><title>Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms</title><title>Journal of intelligent &amp; robotic systems</title><description>This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomechanism including the effects of flexibility and friction. The RLS (recursive least squares) algorithm is adopted for obtaining the optimal consequent parameters of the rules. Within each fuzzy operating region, a local MDPP (minimum degree pole placement) control law with integral action can be constructed based on the estimated local model. Then a fuzzy controller composed of these local MDPP controls can be easily constructed for the servomechanism. The techniques are illustrated using computer simulations.</description><subject>Artificial neural networks</subject><subject>Construction</subject><subject>Dynamical systems</subject><subject>Fuzzy</subject><subject>Fuzzy logic</subject><subject>Fuzzy set theory</subject><subject>Fuzzy sets</subject><subject>Nonlinear dynamics</subject><subject>Servomechanisms</subject><subject>Studies</subject><issn>0921-0296</issn><issn>1573-0409</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2003</creationdate><recordtype>article</recordtype><recordid>eNp9kMFLwzAYxYMoOKtnr8GDnqpJvqTp521Mp8LUMfU80jbRjq6ZSSu6v96JO3nw9IPHjwfvEXLM2TlnAi6GlxtoABSCgZA7ZMCVhpRJhrtkwFDwlAnM9slBjAvGGOYKB2Q2s2Ufgm07-mD74NNxv15_0Xtf2aZuX6lpK7qNrqZTOvJtF3xDnQ903NjPumgsfbLhwy9t-WbaOi7jIdlzpon2aMuEvIyvn0e36eTx5m40nKQrIaFLQWjNwaFRFnlRGK0loJUu4yI30oAzzpWIvBKGAcqKVWXFNTJVlYV1WkNCzn57V8G_9zZ282UdS9s0prW-j3PkMpOZUj_m6b-m0AiZ2JyYkJM_4sL3od2smOc5zzQolcM3tu9sjQ</recordid><startdate>20031001</startdate><enddate>20031001</enddate><creator>Lin, Chorng-shyan</creator><creator>Yang, Tachung</creator><creator>Jou, Yeong-chau</creator><creator>Lin, Lih-chang</creator><general>Springer Nature B.V</general><scope>3V.</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>F28</scope></search><sort><creationdate>20031001</creationdate><title>Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms</title><author>Lin, Chorng-shyan ; Yang, Tachung ; Jou, Yeong-chau ; Lin, Lih-chang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p243t-327713f9a5e91bba77439e4f6128a4a3faffc991d2a0394d0dcd17905dcbef773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Artificial neural networks</topic><topic>Construction</topic><topic>Dynamical systems</topic><topic>Fuzzy</topic><topic>Fuzzy logic</topic><topic>Fuzzy set theory</topic><topic>Fuzzy sets</topic><topic>Nonlinear dynamics</topic><topic>Servomechanisms</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Chorng-shyan</creatorcontrib><creatorcontrib>Yang, Tachung</creatorcontrib><creatorcontrib>Jou, Yeong-chau</creatorcontrib><creatorcontrib>Lin, Lih-chang</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics &amp; Communications Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer science database</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Engineering Database</collection><collection>ProQuest advanced technologies &amp; aerospace journals</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering collection</collection><collection>ProQuest Central Basic</collection><collection>ANTE: Abstracts in New Technology &amp; Engineering</collection><jtitle>Journal of intelligent &amp; robotic systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Chorng-shyan</au><au>Yang, Tachung</au><au>Jou, Yeong-chau</au><au>Lin, Lih-chang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms</atitle><jtitle>Journal of intelligent &amp; robotic systems</jtitle><date>2003-10-01</date><risdate>2003</risdate><volume>38</volume><issue>2</issue><spage>213</spage><epage>235</epage><pages>213-235</pages><issn>0921-0296</issn><eissn>1573-0409</eissn><abstract>This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomechanism including the effects of flexibility and friction. The RLS (recursive least squares) algorithm is adopted for obtaining the optimal consequent parameters of the rules. Within each fuzzy operating region, a local MDPP (minimum degree pole placement) control law with integral action can be constructed based on the estimated local model. Then a fuzzy controller composed of these local MDPP controls can be easily constructed for the servomechanism. The techniques are illustrated using computer simulations.</abstract><cop>Dordrecht</cop><pub>Springer Nature B.V</pub><doi>10.1023/A:1027339220324</doi><tpages>23</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0921-0296
ispartof Journal of intelligent & robotic systems, 2003-10, Vol.38 (2), p.213-235
issn 0921-0296
1573-0409
language eng
recordid cdi_proquest_miscellaneous_914646557
source Springer Link
subjects Artificial neural networks
Construction
Dynamical systems
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Nonlinear dynamics
Servomechanisms
Studies
title Recurrent Neuro-Fuzzy Modeling and Fuzzy MDPP Control for Flexible Servomechanisms
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-27T20%3A48%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Recurrent%20Neuro-Fuzzy%20Modeling%20and%20Fuzzy%20MDPP%20Control%20for%20Flexible%20Servomechanisms&rft.jtitle=Journal%20of%20intelligent%20&%20robotic%20systems&rft.au=Lin,%20Chorng-shyan&rft.date=2003-10-01&rft.volume=38&rft.issue=2&rft.spage=213&rft.epage=235&rft.pages=213-235&rft.issn=0921-0296&rft.eissn=1573-0409&rft_id=info:doi/10.1023/A:1027339220324&rft_dat=%3Cproquest%3E914646557%3C/proquest%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-p243t-327713f9a5e91bba77439e4f6128a4a3faffc991d2a0394d0dcd17905dcbef773%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=881673558&rft_id=info:pmid/&rfr_iscdi=true