Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems

In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of no...

Full description

Saved in:
Bibliographic Details
Published in:Modelling and simulation in engineering 2020, Vol.2020, p.1-13
Main Authors: El Hamidi, Khadija, Mjahed, Mostafa, El Kari, Abdeljalil, Ayad, Hassan
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Artificial neural networks
Autoregressive moving average
Back propagation
Back propagation networks
Comparative studies
Controllers
Dynamical systems
Mathematical models
Multilayers
Neural networks
Neurons
Nonlinear control
Nonlinear dynamics
Nonlinear systems
Recurrent neural networks
Stability
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c451t-282319c4837196f29ced6624f8de1b5d286a87be29ed5ba962f14120d2656dca3
cites cdi_FETCH-LOGICAL-c451t-282319c4837196f29ced6624f8de1b5d286a87be29ed5ba962f14120d2656dca3
container_end_page 13
container_issue
container_start_page 1
container_title Modelling and simulation in engineering
container_volume 2020
creator El Hamidi, Khadija
Mjahed, Mostafa
El Kari, Abdeljalil
Ayad, Hassan
description In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.
doi_str_mv 10.1155/2020/8642915
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_90ce90247eb545bf89313dc8b3f9642e</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_90ce90247eb545bf89313dc8b3f9642e</doaj_id><sourcerecordid>2440438843</sourcerecordid><originalsourceid>FETCH-LOGICAL-c451t-282319c4837196f29ced6624f8de1b5d286a87be29ed5ba962f14120d2656dca3</originalsourceid><addsrcrecordid>eNp9kTlPAzEQhVcIJBDQ8QMsUUKI77XLKFyROAqgorC89mwwbNbB3hDy79kQoKR6o9GnN8criiOCzwgRYkgxxUMlOdVEbBV7RKpyICQW27-10GS3OMw5VJjzUjAm8V7xPPJ23oUPQOPYdik26CmHdoruYJFs00u3jOktI9t6NJrPU_wMM9sBuo0emozqmNBdbJvQgk3ofNXaWXDoYZU7mOWDYqe2TYbDH90vni4vHsfXg5v7q8l4dDNwXJBuQBVlRDuuWEm0rKl24KWkvFYeSCU8VdKqsgKqwYvKaklrwgnFnkohvbNsv5hsfH20r2ae-g3TykQbzHcjpqmxqQuuAaOxA40pL6ESXFS10oww71TFat2_Dnqv441Xf-r7AnJnXuMitf36hnKOOVOKs5463VAuxZwT1H9TCTbrNMw6DfOTRo-fbPCX0Hq7DP_TX5UfiEE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2440438843</pqid></control><display><type>article</type><title>Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems</title><source>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</source><source>Open Access: Wiley-Blackwell Open Access Journals</source><creator>El Hamidi, Khadija ; Mjahed, Mostafa ; El Kari, Abdeljalil ; Ayad, Hassan</creator><contributor>Calì, Michele ; Michele Calì</contributor><creatorcontrib>El Hamidi, Khadija ; Mjahed, Mostafa ; El Kari, Abdeljalil ; Ayad, Hassan ; Calì, Michele ; Michele Calì</creatorcontrib><description>In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.</description><identifier>ISSN: 1687-5591</identifier><identifier>EISSN: 1687-5605</identifier><identifier>DOI: 10.1155/2020/8642915</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Adaptive control ; Algorithms ; Artificial neural networks ; Autoregressive moving average ; Back propagation ; Back propagation networks ; Comparative studies ; Controllers ; Dynamical systems ; Mathematical models ; Multilayers ; Neural networks ; Neurons ; Nonlinear control ; Nonlinear dynamics ; Nonlinear systems ; Recurrent neural networks ; Stability</subject><ispartof>Modelling and simulation in engineering, 2020, Vol.2020, p.1-13</ispartof><rights>Copyright © 2020 Khadija El Hamidi et al.</rights><rights>Copyright © 2020 Khadija El Hamidi et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. http://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-282319c4837196f29ced6624f8de1b5d286a87be29ed5ba962f14120d2656dca3</citedby><cites>FETCH-LOGICAL-c451t-282319c4837196f29ced6624f8de1b5d286a87be29ed5ba962f14120d2656dca3</cites><orcidid>0000-0001-7661-2095 ; 0000-0002-1322-0083</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2440438843/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2440438843?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,4024,25753,27923,27924,27925,37012,44590,75126</link.rule.ids></links><search><contributor>Calì, Michele</contributor><contributor>Michele Calì</contributor><creatorcontrib>El Hamidi, Khadija</creatorcontrib><creatorcontrib>Mjahed, Mostafa</creatorcontrib><creatorcontrib>El Kari, Abdeljalil</creatorcontrib><creatorcontrib>Ayad, Hassan</creatorcontrib><title>Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems</title><title>Modelling and simulation in engineering</title><description>In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.</description><subject>Adaptive control</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Autoregressive moving average</subject><subject>Back propagation</subject><subject>Back propagation networks</subject><subject>Comparative studies</subject><subject>Controllers</subject><subject>Dynamical systems</subject><subject>Mathematical models</subject><subject>Multilayers</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Nonlinear control</subject><subject>Nonlinear dynamics</subject><subject>Nonlinear systems</subject><subject>Recurrent neural networks</subject><subject>Stability</subject><issn>1687-5591</issn><issn>1687-5605</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9kTlPAzEQhVcIJBDQ8QMsUUKI77XLKFyROAqgorC89mwwbNbB3hDy79kQoKR6o9GnN8criiOCzwgRYkgxxUMlOdVEbBV7RKpyICQW27-10GS3OMw5VJjzUjAm8V7xPPJ23oUPQOPYdik26CmHdoruYJFs00u3jOktI9t6NJrPU_wMM9sBuo0emozqmNBdbJvQgk3ofNXaWXDoYZU7mOWDYqe2TYbDH90vni4vHsfXg5v7q8l4dDNwXJBuQBVlRDuuWEm0rKl24KWkvFYeSCU8VdKqsgKqwYvKaklrwgnFnkohvbNsv5hsfH20r2ae-g3TykQbzHcjpqmxqQuuAaOxA40pL6ESXFS10oww71TFat2_Dnqv441Xf-r7AnJnXuMitf36hnKOOVOKs5463VAuxZwT1H9TCTbrNMw6DfOTRo-fbPCX0Hq7DP_TX5UfiEE</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>El Hamidi, Khadija</creator><creator>Mjahed, Mostafa</creator><creator>El Kari, Abdeljalil</creator><creator>Ayad, Hassan</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</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>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-7661-2095</orcidid><orcidid>https://orcid.org/0000-0002-1322-0083</orcidid></search><sort><creationdate>2020</creationdate><title>Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems</title><author>El Hamidi, Khadija ; Mjahed, Mostafa ; El Kari, Abdeljalil ; Ayad, Hassan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c451t-282319c4837196f29ced6624f8de1b5d286a87be29ed5ba962f14120d2656dca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adaptive control</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Autoregressive moving average</topic><topic>Back propagation</topic><topic>Back propagation networks</topic><topic>Comparative studies</topic><topic>Controllers</topic><topic>Dynamical systems</topic><topic>Mathematical models</topic><topic>Multilayers</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Nonlinear control</topic><topic>Nonlinear dynamics</topic><topic>Nonlinear systems</topic><topic>Recurrent neural networks</topic><topic>Stability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>El Hamidi, Khadija</creatorcontrib><creatorcontrib>Mjahed, Mostafa</creatorcontrib><creatorcontrib>El Kari, Abdeljalil</creatorcontrib><creatorcontrib>Ayad, Hassan</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East &amp; Africa Database</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</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>Engineering Database</collection><collection>Advanced Technologies &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; Aerospace Collection</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</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>DOAJ Directory of Open Access Journals</collection><jtitle>Modelling and simulation in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>El Hamidi, Khadija</au><au>Mjahed, Mostafa</au><au>El Kari, Abdeljalil</au><au>Ayad, Hassan</au><au>Calì, Michele</au><au>Michele Calì</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems</atitle><jtitle>Modelling and simulation in engineering</jtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><spage>1</spage><epage>13</epage><pages>1-13</pages><issn>1687-5591</issn><eissn>1687-5605</eissn><abstract>In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2020/8642915</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-7661-2095</orcidid><orcidid>https://orcid.org/0000-0002-1322-0083</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1687-5591
ispartof Modelling and simulation in engineering, 2020, Vol.2020, p.1-13
issn 1687-5591
1687-5605
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_90ce90247eb545bf89313dc8b3f9642e
source Publicly Available Content Database (Proquest) (PQ_SDU_P3); Open Access: Wiley-Blackwell Open Access Journals
subjects Adaptive control
Algorithms
Artificial neural networks
Autoregressive moving average
Back propagation
Back propagation networks
Comparative studies
Controllers
Dynamical systems
Mathematical models
Multilayers
Neural networks
Neurons
Nonlinear control
Nonlinear dynamics
Nonlinear systems
Recurrent neural networks
Stability
title Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T08%3A50%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Adaptive%20Control%20Using%20Neural%20Networks%20and%20Approximate%20Models%20for%20Nonlinear%20Dynamic%20Systems&rft.jtitle=Modelling%20and%20simulation%20in%20engineering&rft.au=El%20Hamidi,%20Khadija&rft.date=2020&rft.volume=2020&rft.spage=1&rft.epage=13&rft.pages=1-13&rft.issn=1687-5591&rft.eissn=1687-5605&rft_id=info:doi/10.1155/2020/8642915&rft_dat=%3Cproquest_doaj_%3E2440438843%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c451t-282319c4837196f29ced6624f8de1b5d286a87be29ed5ba962f14120d2656dca3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2440438843&rft_id=info:pmid/&rfr_iscdi=true