Multi-model estimation using neural network and fault detection in unknown time continuous fractional order nonlinear systems
In this paper, multi-model estimation and fault detection using neural network is proposed for an unknown time continuous fractional order nonlinear system. Fractional differentiation is considered based on Caputo concept and the fractional order is considered to be between 0 and 1. In order to esti...
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| Veröffentlicht in: | Transactions of the Institute of Measurement and Control 2021-02, Vol.43 (3), p.497-509, Article 0142331220932376 |
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| creator | Nassajian, Gholamreza Balochian, Saeed |
| description | In this paper, multi-model estimation and fault detection using neural network is proposed for an unknown time continuous fractional order nonlinear system. Fractional differentiation is considered based on Caputo concept and the fractional order is considered to be between 0 and 1. In order to estimate a time continuous fractional order nonlinear system with unknown term in its dynamic, single-layer and double-layer RBF neural network is used. First, a parallel-series neural network observer is designed for state estimation. Weights of the neural network are updated adaptively and updating laws are presented in fractional order form. Using Lyapunov method, it is proved that state estimation error and weight estimation error of the neural network are bounded. Parameters of the neural estimator converge to ideal parameters which satisfy excitation condition stability. Then, multi-model estimation structure of fractional order nonlinear systems is presented and its application in fault detection is investigated. Finally, simulation results are presented to show efficiency of the proposed method. |
| doi_str_mv | 10.1177/0142331220932376 |
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Fractional differentiation is considered based on Caputo concept and the fractional order is considered to be between 0 and 1. In order to estimate a time continuous fractional order nonlinear system with unknown term in its dynamic, single-layer and double-layer RBF neural network is used. First, a parallel-series neural network observer is designed for state estimation. Weights of the neural network are updated adaptively and updating laws are presented in fractional order form. Using Lyapunov method, it is proved that state estimation error and weight estimation error of the neural network are bounded. Parameters of the neural estimator converge to ideal parameters which satisfy excitation condition stability. Then, multi-model estimation structure of fractional order nonlinear systems is presented and its application in fault detection is investigated. 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Fractional differentiation is considered based on Caputo concept and the fractional order is considered to be between 0 and 1. In order to estimate a time continuous fractional order nonlinear system with unknown term in its dynamic, single-layer and double-layer RBF neural network is used. First, a parallel-series neural network observer is designed for state estimation. Weights of the neural network are updated adaptively and updating laws are presented in fractional order form. Using Lyapunov method, it is proved that state estimation error and weight estimation error of the neural network are bounded. Parameters of the neural estimator converge to ideal parameters which satisfy excitation condition stability. Then, multi-model estimation structure of fractional order nonlinear systems is presented and its application in fault detection is investigated. Finally, simulation results are presented to show efficiency of the proposed method.</description><subject>Automation & Control Systems</subject><subject>Fault detection</subject><subject>Instruments & Instrumentation</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Nonlinear systems</subject><subject>Parameter estimation</subject><subject>Science & Technology</subject><subject>State estimation</subject><subject>Structural stability</subject><subject>Technology</subject><issn>0142-3312</issn><issn>1477-0369</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><recordid>eNqNkctLAzEQh4MoWB93jwGPspp3do9SfIHiRc9Lmp0tqdtEkyzFg_-7aSsKguBpAvN9w8wvCJ1Qck6p1heECsY5ZYw0nHGtdtCECq0rwlWziybrdrXu76ODlBaEECGUmKCPh3HIrlqGDgYMKbulyS54PCbn59jDGM1QSl6F-IKN73BvioA7yGA3oCusf_Fh5XGRAdvgs_NjGBPuo9kwZUKIHUTsgx-cBxNxek8ZlukI7fVmSHD8VQ_R8_XV0_S2un-8uZte3leWkyZXHWNcNrauBRAtWa2NbjpgUlkhLOG1UTNhmdKN6XvKapC1Nb2UtOmYooQZfohOt3NfY3gby5ntIoyxLJZaJmoluCRUFYpsKRtDShH69jWWPOJ7S0m7Drn9HXJRzrbKCmahT9aBt_CtlZSlkI2kurwILXT9f3rq8uYrpmH0uajVVk1mDj_b_7nYJ3S2nns</recordid><startdate>202102</startdate><enddate>202102</enddate><creator>Nassajian, Gholamreza</creator><creator>Balochian, Saeed</creator><general>SAGE Publications</general><general>Sage</general><general>Sage Publications Ltd</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-3137-9167</orcidid></search><sort><creationdate>202102</creationdate><title>Multi-model estimation using neural network and fault detection in unknown time continuous fractional order nonlinear systems</title><author>Nassajian, Gholamreza ; Balochian, Saeed</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c309t-d22359c884e075287a79de256c44c038a6b4c2679aff128e58caf5519d26102a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Automation & Control Systems</topic><topic>Fault detection</topic><topic>Instruments & Instrumentation</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Nonlinear systems</topic><topic>Parameter estimation</topic><topic>Science & Technology</topic><topic>State estimation</topic><topic>Structural stability</topic><topic>Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nassajian, Gholamreza</creatorcontrib><creatorcontrib>Balochian, Saeed</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Transactions of the Institute of Measurement and Control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Nassajian, Gholamreza</au><au>Balochian, Saeed</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multi-model estimation using neural network and fault detection in unknown time continuous fractional order nonlinear systems</atitle><jtitle>Transactions of the Institute of Measurement and Control</jtitle><stitle>T I MEAS CONTROL</stitle><date>2021-02</date><risdate>2021</risdate><volume>43</volume><issue>3</issue><spage>497</spage><epage>509</epage><pages>497-509</pages><artnum>0142331220932376</artnum><issn>0142-3312</issn><eissn>1477-0369</eissn><abstract>In this paper, multi-model estimation and fault detection using neural network is proposed for an unknown time continuous fractional order nonlinear system. Fractional differentiation is considered based on Caputo concept and the fractional order is considered to be between 0 and 1. In order to estimate a time continuous fractional order nonlinear system with unknown term in its dynamic, single-layer and double-layer RBF neural network is used. First, a parallel-series neural network observer is designed for state estimation. Weights of the neural network are updated adaptively and updating laws are presented in fractional order form. Using Lyapunov method, it is proved that state estimation error and weight estimation error of the neural network are bounded. Parameters of the neural estimator converge to ideal parameters which satisfy excitation condition stability. Then, multi-model estimation structure of fractional order nonlinear systems is presented and its application in fault detection is investigated. 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| subjects | Automation & Control Systems Fault detection Instruments & Instrumentation Mathematical models Neural networks Nonlinear systems Parameter estimation Science & Technology State estimation Structural stability Technology |
| title | Multi-model estimation using neural network and fault detection in unknown time continuous fractional order nonlinear systems |
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