Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System
A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets...
Gespeichert in:
| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2008-01, Vol.19 (1), p.54-70 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 70 |
|---|---|
| container_issue | 1 |
| container_start_page | 54 |
| container_title | IEEE transaction on neural networks and learning systems |
| container_volume | 19 |
| creator | WAI, Rong-Jong LEE, Jeng-Dao |
| description | A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies. |
| doi_str_mv | 10.1109/TNN.2007.900814 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_pubmed_primary_18269938</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4359190</ieee_id><sourcerecordid>2545533151</sourcerecordid><originalsourceid>FETCH-LOGICAL-c404t-ac72c0323a62a52a888bb43a4f276d044b5e27791736de743bd70f80abbbaaa13</originalsourceid><addsrcrecordid>eNp90U1r4zAQBmCxbNl-nvdQKGahuycnow9b0rGEZruQpoemZzG25cWpY2Ulu0vy66uQ0EIPPY1AzwzMvIR8pzCiFPR4MZ-PGIAcaQBFxRdyQrWgKYDmX-MbRJZqxuQxOQ1hCUBFBvk3ckwVy7Xm6oRMbypc982LTabDdrtJ53bw2MbS_3f-OZm4rveuTWrnk3v829qXZOGxC2vne-wb1yWPm9Db1Tk5qrEN9uJQz8jT9HYxuUtnD7__TG5maSlA9CmWkpXAGcecYcZQKVUUgqOomcwrEKLILJNSU8nzykrBi0pCrQCLokBEys_Ir_3ctXf_Bht6s2pCadsWO-uGYJTMQFOqsih_fiolMJ3ngkf44wNcusF3cQujKeNSyhwiGu9R6V0I3tZm7ZsV-o2hYHZJmJiE2SVh9knEjqvD2KFY2erdH04fwfUBYCixreNZyya8uTgLMs13K1_uXWOtffsWPNNUA38Fa4yYPQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>912377760</pqid></control><display><type>article</type><title>Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System</title><source>IEEE Electronic Library (IEL)</source><creator>WAI, Rong-Jong ; LEE, Jeng-Dao</creator><creatorcontrib>WAI, Rong-Jong ; LEE, Jeng-Dao</creatorcontrib><description>A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.</description><identifier>ISSN: 1045-9227</identifier><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 1941-0093</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNN.2007.900814</identifier><identifier>PMID: 18269938</identifier><identifier>CODEN: ITNNEP</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Adaptive control ; Adaptive control systems ; Algorithmics. Computability. Computer arithmetics ; Algorithms ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Control systems ; Design ; Electromagnetic modeling ; Electromagnets ; Exact sciences and technology ; Feedback ; Fuzzy Logic ; Fuzzy neural network (FNN) ; Ground, air and sea transportation, marine construction ; Humans ; linear induction motor (LIM) ; maglev transportation system ; Magnetic levitation ; magnetic levitation (maglev) ; Magnetic levitation systems ; Magnetic levitation vehicles ; Mathematical models ; Neural Networks (Computer) ; Nonlinear dynamical systems ; Nonlinear Dynamics ; Pattern Recognition, Automated ; Programmable control ; Sheet molding compounds ; Signal Processing, Computer-Assisted ; Sliding mode control ; sliding-mode control (SMC) ; Strategy ; Studies ; Theoretical computing ; Transportation ; Transportation systems ; Uncertainty</subject><ispartof>IEEE transaction on neural networks and learning systems, 2008-01, Vol.19 (1), p.54-70</ispartof><rights>2008 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2008</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c404t-ac72c0323a62a52a888bb43a4f276d044b5e27791736de743bd70f80abbbaaa13</citedby><cites>FETCH-LOGICAL-c404t-ac72c0323a62a52a888bb43a4f276d044b5e27791736de743bd70f80abbbaaa13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4359190$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,4010,27900,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4359190$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20005931$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18269938$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>WAI, Rong-Jong</creatorcontrib><creatorcontrib>LEE, Jeng-Dao</creatorcontrib><title>Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNN</addtitle><addtitle>IEEE Trans Neural Netw</addtitle><description>A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.</description><subject>Adaptive control</subject><subject>Adaptive control systems</subject><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Control systems</subject><subject>Design</subject><subject>Electromagnetic modeling</subject><subject>Electromagnets</subject><subject>Exact sciences and technology</subject><subject>Feedback</subject><subject>Fuzzy Logic</subject><subject>Fuzzy neural network (FNN)</subject><subject>Ground, air and sea transportation, marine construction</subject><subject>Humans</subject><subject>linear induction motor (LIM)</subject><subject>maglev transportation system</subject><subject>Magnetic levitation</subject><subject>magnetic levitation (maglev)</subject><subject>Magnetic levitation systems</subject><subject>Magnetic levitation vehicles</subject><subject>Mathematical models</subject><subject>Neural Networks (Computer)</subject><subject>Nonlinear dynamical systems</subject><subject>Nonlinear Dynamics</subject><subject>Pattern Recognition, Automated</subject><subject>Programmable control</subject><subject>Sheet molding compounds</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Sliding mode control</subject><subject>sliding-mode control (SMC)</subject><subject>Strategy</subject><subject>Studies</subject><subject>Theoretical computing</subject><subject>Transportation</subject><subject>Transportation systems</subject><subject>Uncertainty</subject><issn>1045-9227</issn><issn>2162-237X</issn><issn>1941-0093</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNp90U1r4zAQBmCxbNl-nvdQKGahuycnow9b0rGEZruQpoemZzG25cWpY2Ulu0vy66uQ0EIPPY1AzwzMvIR8pzCiFPR4MZ-PGIAcaQBFxRdyQrWgKYDmX-MbRJZqxuQxOQ1hCUBFBvk3ckwVy7Xm6oRMbypc982LTabDdrtJ53bw2MbS_3f-OZm4rveuTWrnk3v829qXZOGxC2vne-wb1yWPm9Db1Tk5qrEN9uJQz8jT9HYxuUtnD7__TG5maSlA9CmWkpXAGcecYcZQKVUUgqOomcwrEKLILJNSU8nzykrBi0pCrQCLokBEys_Ir_3ctXf_Bht6s2pCadsWO-uGYJTMQFOqsih_fiolMJ3ngkf44wNcusF3cQujKeNSyhwiGu9R6V0I3tZm7ZsV-o2hYHZJmJiE2SVh9knEjqvD2KFY2erdH04fwfUBYCixreNZyya8uTgLMs13K1_uXWOtffsWPNNUA38Fa4yYPQ</recordid><startdate>200801</startdate><enddate>200801</enddate><creator>WAI, Rong-Jong</creator><creator>LEE, Jeng-Dao</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TK</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>200801</creationdate><title>Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System</title><author>WAI, Rong-Jong ; LEE, Jeng-Dao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c404t-ac72c0323a62a52a888bb43a4f276d044b5e27791736de743bd70f80abbbaaa13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Adaptive control</topic><topic>Adaptive control systems</topic><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Control systems</topic><topic>Design</topic><topic>Electromagnetic modeling</topic><topic>Electromagnets</topic><topic>Exact sciences and technology</topic><topic>Feedback</topic><topic>Fuzzy Logic</topic><topic>Fuzzy neural network (FNN)</topic><topic>Ground, air and sea transportation, marine construction</topic><topic>Humans</topic><topic>linear induction motor (LIM)</topic><topic>maglev transportation system</topic><topic>Magnetic levitation</topic><topic>magnetic levitation (maglev)</topic><topic>Magnetic levitation systems</topic><topic>Magnetic levitation vehicles</topic><topic>Mathematical models</topic><topic>Neural Networks (Computer)</topic><topic>Nonlinear dynamical systems</topic><topic>Nonlinear Dynamics</topic><topic>Pattern Recognition, Automated</topic><topic>Programmable control</topic><topic>Sheet molding compounds</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Sliding mode control</topic><topic>sliding-mode control (SMC)</topic><topic>Strategy</topic><topic>Studies</topic><topic>Theoretical computing</topic><topic>Transportation</topic><topic>Transportation systems</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>WAI, Rong-Jong</creatorcontrib><creatorcontrib>LEE, Jeng-Dao</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</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>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>WAI, Rong-Jong</au><au>LEE, Jeng-Dao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNN</stitle><addtitle>IEEE Trans Neural Netw</addtitle><date>2008-01</date><risdate>2008</risdate><volume>19</volume><issue>1</issue><spage>54</spage><epage>70</epage><pages>54-70</pages><issn>1045-9227</issn><issn>2162-237X</issn><eissn>1941-0093</eissn><eissn>2162-2388</eissn><coden>ITNNEP</coden><abstract>A magnetic-levitation (maglev) transportation system including levitation and propulsion control is a subject of considerable scientific interest because of highly nonlinear and unstable behaviors. In this paper, the dynamic model of a maglev transportation system including levitated electromagnets and a propulsive linear induction motor (LIM) based on the concepts of mechanical geometry and motion dynamics is developed first. Then, a model-based sliding-mode control (SMC) strategy is introduced. In order to alleviate chattering phenomena caused by the inappropriate selection of uncertainty bound, a simple bound estimation algorithm is embedded in the SMC strategy to form an adaptive sliding-mode control (ASMC) scheme. However, this estimation algorithm is always a positive value so that tracking errors introduced by any uncertainty will cause the estimated bound increase even to infinity with time. Therefore, it further designs an adaptive fuzzy-neural-network control (AFNNC) scheme by imitating the SMC strategy for the maglev transportation system. In the model-free AFNNC, online learning algorithms are designed to cope with the problem of chattering phenomena caused by the sign action in SMC design, and to ensure the stability of the controlled system without the requirement of auxiliary compensated controllers despite the existence of uncertainties. The outputs of the AFNNC scheme can be directly supplied to the electromagnets and LIM without complicated control transformations for relaxing strict constrains in conventional model-based control methodologies. The effectiveness of the proposed control schemes for the maglev transportation system is verified by numerical simulations, and the superiority of the AFNNC scheme is indicated in comparison with the SMC and ASMC strategies.</abstract><cop>New York, NY</cop><pub>IEEE</pub><pmid>18269938</pmid><doi>10.1109/TNN.2007.900814</doi><tpages>17</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1045-9227 |
| ispartof | IEEE transaction on neural networks and learning systems, 2008-01, Vol.19 (1), p.54-70 |
| issn | 1045-9227 2162-237X 1941-0093 2162-2388 |
| language | eng |
| recordid | cdi_pubmed_primary_18269938 |
| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive control Adaptive control systems Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial intelligence Computer science control theory systems Control systems Design Electromagnetic modeling Electromagnets Exact sciences and technology Feedback Fuzzy Logic Fuzzy neural network (FNN) Ground, air and sea transportation, marine construction Humans linear induction motor (LIM) maglev transportation system Magnetic levitation magnetic levitation (maglev) Magnetic levitation systems Magnetic levitation vehicles Mathematical models Neural Networks (Computer) Nonlinear dynamical systems Nonlinear Dynamics Pattern Recognition, Automated Programmable control Sheet molding compounds Signal Processing, Computer-Assisted Sliding mode control sliding-mode control (SMC) Strategy Studies Theoretical computing Transportation Transportation systems Uncertainty |
| title | Adaptive Fuzzy-Neural-Network Control for Maglev Transportation System |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T18%3A03%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Adaptive%20Fuzzy-Neural-Network%20Control%20for%20Maglev%20Transportation%20System&rft.jtitle=IEEE%20transaction%20on%20neural%20networks%20and%20learning%20systems&rft.au=WAI,%20Rong-Jong&rft.date=2008-01&rft.volume=19&rft.issue=1&rft.spage=54&rft.epage=70&rft.pages=54-70&rft.issn=1045-9227&rft.eissn=1941-0093&rft.coden=ITNNEP&rft_id=info:doi/10.1109/TNN.2007.900814&rft_dat=%3Cproquest_RIE%3E2545533151%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=912377760&rft_id=info:pmid/18269938&rft_ieee_id=4359190&rfr_iscdi=true |