A Modified Fractional-Order Unscented Kalman Filter for Nonlinear Fractional-Order Systems
In this paper, a fractional-order unscented Kalman filter (FUKF) is introduced at first. Then, its convergence is analyzed based on Lyapunov functions for nonlinear fractional-order systems. Specific conditions are obtained that guarantee the boundedness of the FUKF estimation error. In addition, an...
Gespeichert in:
| Veröffentlicht in: | Circuits, systems, and signal processing systems, and signal processing, 2018-09, Vol.37 (9), p.3756-3784 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 3784 |
|---|---|
| container_issue | 9 |
| container_start_page | 3756 |
| container_title | Circuits, systems, and signal processing |
| container_volume | 37 |
| creator | Ramezani, Abdolrahman Safarinejadian, Behrouz |
| description | In this paper, a fractional-order unscented Kalman filter (FUKF) is introduced at first. Then, its convergence is analyzed based on Lyapunov functions for nonlinear fractional-order systems. Specific conditions are obtained that guarantee the boundedness of the FUKF estimation error. In addition, an adaptive noise covariance is suggested to overcome huge estimation errors. Since the adaptation law plays a crucial role in the performance of the proposed method, a fuzzy logic based method is also presented to improve the adaptive noise covariance. Therefore, a modified FUKF is proposed to increase the convergence and the accuracy of the estimation. Finally, the proposed algorithm is implemented to estimate the states of a two electric pendulum system and its performance is analyzed. Simulation results show that a huge estimation error leads to the FUKF divergence; however, the modified fractional-order unscented Kalman filter with fuzzy performs an accurate state estimation. |
| doi_str_mv | 10.1007/s00034-017-0729-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_1982530088</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1982530088</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-731949ca4d66a589b6dcfbaf5a7338c7c3ea81adea4939a2e8c6bc14edf4800a3</originalsourceid><addsrcrecordid>eNp1kMFKAzEQhoMoWKsP4G3Bc3Sy2d0kx1KsFas9aEG8hGmSlS3bbE22h769KetBEE8DM__3M3yEXDO4ZQDiLgIALygwQUHkiqoTMmIlZ7SUQp6SEeRCUpDs_ZxcxLgBYKpQ-Yh8TLLnzjZ142w2C2j6pvPY0mWwLmQrH43zfTo9YbtFn82atk_7ugvZS-fbxjsMf7HXQ-zdNl6Ssxrb6K5-5pisZvdv0zldLB8ep5MFNZxVPRX8-IrBwlYVllKtK2vqNdYlCs6lEYY7lAytw0JxhbmTplobVjhbFxIA-ZjcDL270H3tXez1ptuH9E7UTMm85ABSphQbUiZ0MQZX611othgOmoE-KtSDQp0U6qNCrRKTD0xMWf_pwq_mf6FvXQ10ow</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1982530088</pqid></control><display><type>article</type><title>A Modified Fractional-Order Unscented Kalman Filter for Nonlinear Fractional-Order Systems</title><source>Springer Nature - Complete Springer Journals</source><creator>Ramezani, Abdolrahman ; Safarinejadian, Behrouz</creator><creatorcontrib>Ramezani, Abdolrahman ; Safarinejadian, Behrouz</creatorcontrib><description>In this paper, a fractional-order unscented Kalman filter (FUKF) is introduced at first. Then, its convergence is analyzed based on Lyapunov functions for nonlinear fractional-order systems. Specific conditions are obtained that guarantee the boundedness of the FUKF estimation error. In addition, an adaptive noise covariance is suggested to overcome huge estimation errors. Since the adaptation law plays a crucial role in the performance of the proposed method, a fuzzy logic based method is also presented to improve the adaptive noise covariance. Therefore, a modified FUKF is proposed to increase the convergence and the accuracy of the estimation. Finally, the proposed algorithm is implemented to estimate the states of a two electric pendulum system and its performance is analyzed. Simulation results show that a huge estimation error leads to the FUKF divergence; however, the modified fractional-order unscented Kalman filter with fuzzy performs an accurate state estimation.</description><identifier>ISSN: 0278-081X</identifier><identifier>EISSN: 1531-5878</identifier><identifier>DOI: 10.1007/s00034-017-0729-9</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Circuits and Systems ; Computer simulation ; Convergence ; Covariance ; Divergence ; Electrical Engineering ; Electronics and Microelectronics ; Engineering ; Fuzzy logic ; Instrumentation ; Kalman filters ; Liapunov functions ; Nonlinear systems ; Signal,Image and Speech Processing ; State estimation</subject><ispartof>Circuits, systems, and signal processing, 2018-09, Vol.37 (9), p.3756-3784</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2017</rights><rights>Circuits, Systems, and Signal Processing is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-731949ca4d66a589b6dcfbaf5a7338c7c3ea81adea4939a2e8c6bc14edf4800a3</citedby><cites>FETCH-LOGICAL-c316t-731949ca4d66a589b6dcfbaf5a7338c7c3ea81adea4939a2e8c6bc14edf4800a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00034-017-0729-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00034-017-0729-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Ramezani, Abdolrahman</creatorcontrib><creatorcontrib>Safarinejadian, Behrouz</creatorcontrib><title>A Modified Fractional-Order Unscented Kalman Filter for Nonlinear Fractional-Order Systems</title><title>Circuits, systems, and signal processing</title><addtitle>Circuits Syst Signal Process</addtitle><description>In this paper, a fractional-order unscented Kalman filter (FUKF) is introduced at first. Then, its convergence is analyzed based on Lyapunov functions for nonlinear fractional-order systems. Specific conditions are obtained that guarantee the boundedness of the FUKF estimation error. In addition, an adaptive noise covariance is suggested to overcome huge estimation errors. Since the adaptation law plays a crucial role in the performance of the proposed method, a fuzzy logic based method is also presented to improve the adaptive noise covariance. Therefore, a modified FUKF is proposed to increase the convergence and the accuracy of the estimation. Finally, the proposed algorithm is implemented to estimate the states of a two electric pendulum system and its performance is analyzed. Simulation results show that a huge estimation error leads to the FUKF divergence; however, the modified fractional-order unscented Kalman filter with fuzzy performs an accurate state estimation.</description><subject>Circuits and Systems</subject><subject>Computer simulation</subject><subject>Convergence</subject><subject>Covariance</subject><subject>Divergence</subject><subject>Electrical Engineering</subject><subject>Electronics and Microelectronics</subject><subject>Engineering</subject><subject>Fuzzy logic</subject><subject>Instrumentation</subject><subject>Kalman filters</subject><subject>Liapunov functions</subject><subject>Nonlinear systems</subject><subject>Signal,Image and Speech Processing</subject><subject>State estimation</subject><issn>0278-081X</issn><issn>1531-5878</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kMFKAzEQhoMoWKsP4G3Bc3Sy2d0kx1KsFas9aEG8hGmSlS3bbE22h769KetBEE8DM__3M3yEXDO4ZQDiLgIALygwQUHkiqoTMmIlZ7SUQp6SEeRCUpDs_ZxcxLgBYKpQ-Yh8TLLnzjZ142w2C2j6pvPY0mWwLmQrH43zfTo9YbtFn82atk_7ugvZS-fbxjsMf7HXQ-zdNl6Ssxrb6K5-5pisZvdv0zldLB8ep5MFNZxVPRX8-IrBwlYVllKtK2vqNdYlCs6lEYY7lAytw0JxhbmTplobVjhbFxIA-ZjcDL270H3tXez1ptuH9E7UTMm85ABSphQbUiZ0MQZX611othgOmoE-KtSDQp0U6qNCrRKTD0xMWf_pwq_mf6FvXQ10ow</recordid><startdate>20180901</startdate><enddate>20180901</enddate><creator>Ramezani, Abdolrahman</creator><creator>Safarinejadian, Behrouz</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7SP</scope><scope>7XB</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</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>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>M2P</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>S0W</scope></search><sort><creationdate>20180901</creationdate><title>A Modified Fractional-Order Unscented Kalman Filter for Nonlinear Fractional-Order Systems</title><author>Ramezani, Abdolrahman ; Safarinejadian, Behrouz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-731949ca4d66a589b6dcfbaf5a7338c7c3ea81adea4939a2e8c6bc14edf4800a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Circuits and Systems</topic><topic>Computer simulation</topic><topic>Convergence</topic><topic>Covariance</topic><topic>Divergence</topic><topic>Electrical Engineering</topic><topic>Electronics and Microelectronics</topic><topic>Engineering</topic><topic>Fuzzy logic</topic><topic>Instrumentation</topic><topic>Kalman filters</topic><topic>Liapunov functions</topic><topic>Nonlinear systems</topic><topic>Signal,Image and Speech Processing</topic><topic>State estimation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ramezani, Abdolrahman</creatorcontrib><creatorcontrib>Safarinejadian, Behrouz</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</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 & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</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>Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & 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>DELNET Engineering & Technology Collection</collection><jtitle>Circuits, systems, and signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ramezani, Abdolrahman</au><au>Safarinejadian, Behrouz</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Modified Fractional-Order Unscented Kalman Filter for Nonlinear Fractional-Order Systems</atitle><jtitle>Circuits, systems, and signal processing</jtitle><stitle>Circuits Syst Signal Process</stitle><date>2018-09-01</date><risdate>2018</risdate><volume>37</volume><issue>9</issue><spage>3756</spage><epage>3784</epage><pages>3756-3784</pages><issn>0278-081X</issn><eissn>1531-5878</eissn><abstract>In this paper, a fractional-order unscented Kalman filter (FUKF) is introduced at first. Then, its convergence is analyzed based on Lyapunov functions for nonlinear fractional-order systems. Specific conditions are obtained that guarantee the boundedness of the FUKF estimation error. In addition, an adaptive noise covariance is suggested to overcome huge estimation errors. Since the adaptation law plays a crucial role in the performance of the proposed method, a fuzzy logic based method is also presented to improve the adaptive noise covariance. Therefore, a modified FUKF is proposed to increase the convergence and the accuracy of the estimation. Finally, the proposed algorithm is implemented to estimate the states of a two electric pendulum system and its performance is analyzed. Simulation results show that a huge estimation error leads to the FUKF divergence; however, the modified fractional-order unscented Kalman filter with fuzzy performs an accurate state estimation.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s00034-017-0729-9</doi><tpages>29</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0278-081X |
| ispartof | Circuits, systems, and signal processing, 2018-09, Vol.37 (9), p.3756-3784 |
| issn | 0278-081X 1531-5878 |
| language | eng |
| recordid | cdi_proquest_journals_1982530088 |
| source | Springer Nature - Complete Springer Journals |
| subjects | Circuits and Systems Computer simulation Convergence Covariance Divergence Electrical Engineering Electronics and Microelectronics Engineering Fuzzy logic Instrumentation Kalman filters Liapunov functions Nonlinear systems Signal,Image and Speech Processing State estimation |
| title | A Modified Fractional-Order Unscented Kalman Filter for Nonlinear Fractional-Order Systems |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T01%3A42%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Modified%20Fractional-Order%20Unscented%20Kalman%20Filter%20for%20Nonlinear%20Fractional-Order%20Systems&rft.jtitle=Circuits,%20systems,%20and%20signal%20processing&rft.au=Ramezani,%20Abdolrahman&rft.date=2018-09-01&rft.volume=37&rft.issue=9&rft.spage=3756&rft.epage=3784&rft.pages=3756-3784&rft.issn=0278-081X&rft.eissn=1531-5878&rft_id=info:doi/10.1007/s00034-017-0729-9&rft_dat=%3Cproquest_cross%3E1982530088%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1982530088&rft_id=info:pmid/&rfr_iscdi=true |