Intelligent Terminal Sliding Mode Control of Active Power Filters by Self-Evolving Emotional Neural Network
In this article, a control system based on evolutionary emotional neural network is proposed for active power filters (APFs) to improve power quality. First, the dynamic model of the APF containing external disturbances and component parameter perturbations is introduced. The global fast terminal sl...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2023-04, Vol.19 (4), p.6138-6149 |
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| creator | Chu, Yundi Fu, Shili Hou, Shixi Fei, Juntao |
| description | In this article, a control system based on evolutionary emotional neural network is proposed for active power filters (APFs) to improve power quality. First, the dynamic model of the APF containing external disturbances and component parameter perturbations is introduced. The global fast terminal sliding mode (GFTSM) control method is proposed for the APF and its finite-time convergence and global robustness are demonstrated. In addition, an emotional neural network based on Hermite orthogonal polynomials as the activation function is constructed and combined with an evolutionary mechanism to form a self-evolving emotional neural network (SEENN). Then, a model-free control system based on SEENN is designed to address the model dependence of the GFTSM controller design. The parameter update law is designed under the Lyapunov framework to ensure stability. Finally, the results of prototype experiments show the excellent performance of the proposed control algorithm. |
| doi_str_mv | 10.1109/TII.2022.3168654 |
| format | Article |
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First, the dynamic model of the APF containing external disturbances and component parameter perturbations is introduced. The global fast terminal sliding mode (GFTSM) control method is proposed for the APF and its finite-time convergence and global robustness are demonstrated. In addition, an emotional neural network based on Hermite orthogonal polynomials as the activation function is constructed and combined with an evolutionary mechanism to form a self-evolving emotional neural network (SEENN). Then, a model-free control system based on SEENN is designed to address the model dependence of the GFTSM controller design. The parameter update law is designed under the Lyapunov framework to ensure stability. Finally, the results of prototype experiments show the excellent performance of the proposed control algorithm.</description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2022.3168654</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Active control ; Active filters ; Active power filter (APF) ; Algorithms ; Biological neural networks ; Control methods ; Control systems design ; Control theory ; Convergence ; Design parameters ; Dynamic models ; Evolution ; global fast terminal sliding mode (GFTSM) ; hermite orthogonal polynomials activation function ; Hermite polynomials ; Informatics ; Neural networks ; Perturbation ; self-evolving emotional neural network (SEENN) ; Sliding mode control ; Stability analysis ; Uncertainty</subject><ispartof>IEEE transactions on industrial informatics, 2023-04, Vol.19 (4), p.6138-6149</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c291t-80bab5f77acbb645b43c132bdcbee6d3c218c5aaf5c24a899192ed76ae7ff78e3</citedby><cites>FETCH-LOGICAL-c291t-80bab5f77acbb645b43c132bdcbee6d3c218c5aaf5c24a899192ed76ae7ff78e3</cites><orcidid>0000-0002-9179-9836 ; 0000-0002-9544-9120</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9760094$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9760094$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Chu, Yundi</creatorcontrib><creatorcontrib>Fu, Shili</creatorcontrib><creatorcontrib>Hou, Shixi</creatorcontrib><creatorcontrib>Fei, Juntao</creatorcontrib><title>Intelligent Terminal Sliding Mode Control of Active Power Filters by Self-Evolving Emotional Neural Network</title><title>IEEE transactions on industrial informatics</title><addtitle>TII</addtitle><description>In this article, a control system based on evolutionary emotional neural network is proposed for active power filters (APFs) to improve power quality. First, the dynamic model of the APF containing external disturbances and component parameter perturbations is introduced. The global fast terminal sliding mode (GFTSM) control method is proposed for the APF and its finite-time convergence and global robustness are demonstrated. In addition, an emotional neural network based on Hermite orthogonal polynomials as the activation function is constructed and combined with an evolutionary mechanism to form a self-evolving emotional neural network (SEENN). Then, a model-free control system based on SEENN is designed to address the model dependence of the GFTSM controller design. The parameter update law is designed under the Lyapunov framework to ensure stability. Finally, the results of prototype experiments show the excellent performance of the proposed control algorithm.</description><subject>Active control</subject><subject>Active filters</subject><subject>Active power filter (APF)</subject><subject>Algorithms</subject><subject>Biological neural networks</subject><subject>Control methods</subject><subject>Control systems design</subject><subject>Control theory</subject><subject>Convergence</subject><subject>Design parameters</subject><subject>Dynamic models</subject><subject>Evolution</subject><subject>global fast terminal sliding mode (GFTSM)</subject><subject>hermite orthogonal polynomials activation function</subject><subject>Hermite polynomials</subject><subject>Informatics</subject><subject>Neural networks</subject><subject>Perturbation</subject><subject>self-evolving emotional neural network (SEENN)</subject><subject>Sliding mode control</subject><subject>Stability analysis</subject><subject>Uncertainty</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtrAjEUhYfSQq3tvtBNoOuxecwrSxFtB-wDtOshydxINE5sJir--85U6ercxfkOly-KHgkeEYL5y7IsRxRTOmIkK7I0uYoGhCckxjjF192dpiRmFLPb6K5t1xizHDM-iDZlE8Bas4ImoCX4rWmERQtratOs0LurAU1cE7yzyGk0VsEcAH25I3g0MzaAb5E8oQVYHU8Pzh56arp1wbh-5wP2_i_C0fnNfXSjhW3h4ZLD6Hs2XU7e4vnnazkZz2NFOQlxgaWQqc5zoaTMklQmTBFGZa0kQFYzRUmhUiF0qmgiCs4Jp1DnmYBc67wANoyez7s773720IZq7fa--6etaM4xYUVCWNfC55byrm096GrnzVb4U0Vw1SutOqVVr7S6KO2QpzNiAOC_zvMMY56wX1OKc8c</recordid><startdate>20230401</startdate><enddate>20230401</enddate><creator>Chu, Yundi</creator><creator>Fu, Shili</creator><creator>Hou, Shixi</creator><creator>Fei, Juntao</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Active control Active filters Active power filter (APF) Algorithms Biological neural networks Control methods Control systems design Control theory Convergence Design parameters Dynamic models Evolution global fast terminal sliding mode (GFTSM) hermite orthogonal polynomials activation function Hermite polynomials Informatics Neural networks Perturbation self-evolving emotional neural network (SEENN) Sliding mode control Stability analysis Uncertainty |
| title | Intelligent Terminal Sliding Mode Control of Active Power Filters by Self-Evolving Emotional Neural Network |
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