Adaptive control of nonlinear fractional-order systems using T–S fuzzy method
Owing to the superior capability of fractional differential equations in modeling and characterizing accurate dynamical properties of many high technology real world systems, the design and control of fractional-order systems have captured lots of attention in recent decades. In this paper, an adapt...
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Veröffentlicht in: | International journal of machine learning and cybernetics 2019-03, Vol.10 (3), p.527-540 |
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creator | Mirzajani, Saeed Aghababa, Mohammad Pourmahmood Heydari, Aghileh |
description | Owing to the superior capability of fractional differential equations in modeling and characterizing accurate dynamical properties of many high technology real world systems, the design and control of fractional-order systems have captured lots of attention in recent decades. In this paper, an adaptive intelligent fuzzy approach to controlling and stabilization of nonlinear non-autonomous fractional-order systems is proposed. Since dynamic equations of applied fractional-order systems usually contain various parameters and nonlinear terms, the Takagi–Sugeno (T–S) fuzzy models with if-then rules are adopted to describe the system dynamics. Also, as the nonlinear system parameters are assumed to be unknown, adaptive laws are derived to estimate such fluctuations. Simple adaptive linear-like control rules are developed based on the T–S fuzzy control theory. The stability of the resulting closed loop system is guaranteed by Lyapunov’s stability theory. Two illustrative numerical examples are presented to emphasize the correct performance and applicability of the proposed adaptive fuzzy control methodology. It is worth to notice that the proposed controller works well for stabilization of a wide class of either autonomous nonlinear uncertain fractional-order systems or non-autonomous complex systems with unknown parameters. |
doi_str_mv | 10.1007/s13042-017-0733-1 |
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In this paper, an adaptive intelligent fuzzy approach to controlling and stabilization of nonlinear non-autonomous fractional-order systems is proposed. Since dynamic equations of applied fractional-order systems usually contain various parameters and nonlinear terms, the Takagi–Sugeno (T–S) fuzzy models with if-then rules are adopted to describe the system dynamics. Also, as the nonlinear system parameters are assumed to be unknown, adaptive laws are derived to estimate such fluctuations. Simple adaptive linear-like control rules are developed based on the T–S fuzzy control theory. The stability of the resulting closed loop system is guaranteed by Lyapunov’s stability theory. Two illustrative numerical examples are presented to emphasize the correct performance and applicability of the proposed adaptive fuzzy control methodology. It is worth to notice that the proposed controller works well for stabilization of a wide class of either autonomous nonlinear uncertain fractional-order systems or non-autonomous complex systems with unknown parameters.</description><identifier>ISSN: 1868-8071</identifier><identifier>EISSN: 1868-808X</identifier><identifier>DOI: 10.1007/s13042-017-0733-1</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adaptive control ; Artificial Intelligence ; Calculus ; Closed loops ; Complex Systems ; Computational Intelligence ; Control ; Control methods ; Control theory ; Controllers ; Design ; Differential equations ; Dynamical systems ; Engineering ; Equilibrium ; Feedback control ; Fractional calculus ; Fuzzy control ; Fuzzy logic ; Mathematical models ; Mechatronics ; Neural networks ; Nonlinear control ; Nonlinear systems ; Original Article ; Parameters ; Pattern Recognition ; Robotics ; Stabilization ; System dynamics ; Systems Biology</subject><ispartof>International journal of machine learning and cybernetics, 2019-03, Vol.10 (3), p.527-540</ispartof><rights>Springer-Verlag GmbH Germany 2017</rights><rights>Springer-Verlag GmbH Germany 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-1c8ce8407898ea833fd047d61e9144736a06a62207c922dd1c7f28a067e5b93a3</citedby><cites>FETCH-LOGICAL-c316t-1c8ce8407898ea833fd047d61e9144736a06a62207c922dd1c7f28a067e5b93a3</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/s13042-017-0733-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2919505652?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21367,27901,27902,33721,41464,42533,43781,51294</link.rule.ids></links><search><creatorcontrib>Mirzajani, Saeed</creatorcontrib><creatorcontrib>Aghababa, Mohammad Pourmahmood</creatorcontrib><creatorcontrib>Heydari, Aghileh</creatorcontrib><title>Adaptive control of nonlinear fractional-order systems using T–S fuzzy method</title><title>International journal of machine learning and cybernetics</title><addtitle>Int. J. Mach. Learn. & Cyber</addtitle><description>Owing to the superior capability of fractional differential equations in modeling and characterizing accurate dynamical properties of many high technology real world systems, the design and control of fractional-order systems have captured lots of attention in recent decades. In this paper, an adaptive intelligent fuzzy approach to controlling and stabilization of nonlinear non-autonomous fractional-order systems is proposed. Since dynamic equations of applied fractional-order systems usually contain various parameters and nonlinear terms, the Takagi–Sugeno (T–S) fuzzy models with if-then rules are adopted to describe the system dynamics. Also, as the nonlinear system parameters are assumed to be unknown, adaptive laws are derived to estimate such fluctuations. Simple adaptive linear-like control rules are developed based on the T–S fuzzy control theory. The stability of the resulting closed loop system is guaranteed by Lyapunov’s stability theory. Two illustrative numerical examples are presented to emphasize the correct performance and applicability of the proposed adaptive fuzzy control methodology. It is worth to notice that the proposed controller works well for stabilization of a wide class of either autonomous nonlinear uncertain fractional-order systems or non-autonomous complex systems with unknown parameters.</description><subject>Adaptive control</subject><subject>Artificial Intelligence</subject><subject>Calculus</subject><subject>Closed loops</subject><subject>Complex Systems</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Control methods</subject><subject>Control theory</subject><subject>Controllers</subject><subject>Design</subject><subject>Differential equations</subject><subject>Dynamical systems</subject><subject>Engineering</subject><subject>Equilibrium</subject><subject>Feedback control</subject><subject>Fractional calculus</subject><subject>Fuzzy control</subject><subject>Fuzzy logic</subject><subject>Mathematical models</subject><subject>Mechatronics</subject><subject>Neural networks</subject><subject>Nonlinear control</subject><subject>Nonlinear systems</subject><subject>Original Article</subject><subject>Parameters</subject><subject>Pattern Recognition</subject><subject>Robotics</subject><subject>Stabilization</subject><subject>System dynamics</subject><subject>Systems Biology</subject><issn>1868-8071</issn><issn>1868-808X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp1kM1KAzEUhYMoWLQP4C7gOnpvMpNklqX4B4UurOAuxEymTplOajIV2pXv4Bv6JE4Z0ZV3cy-Xcw6Hj5ALhCsEUNcJBWScASoGSgiGR2SEWmqmQT8f_94KT8k4pRX0I0EI4CMyn5R209XvnrrQdjE0NFS0DW1Tt95GWkXrujq0tmEhlj7StEudXye6TXW7pIuvj89HWm33-x1d--41lOfkpLJN8uOffUaebm8W03s2m989TCcz5gTKjqHTzusMlC60t1qIqoRMlRJ9gVmmhLQgreQclCs4L0t0quK6fyqfvxTCijNyOeRuYnjb-tSZVdjGvmcyvMAih1zmvFfhoHIxpBR9ZTaxXtu4MwjmgM4M6EyPzhzQGew9fPCkXtsuffxL_t_0DWNrcTo</recordid><startdate>20190301</startdate><enddate>20190301</enddate><creator>Mirzajani, Saeed</creator><creator>Aghababa, Mohammad Pourmahmood</creator><creator>Heydari, Aghileh</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</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>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20190301</creationdate><title>Adaptive control of nonlinear fractional-order systems using T–S fuzzy method</title><author>Mirzajani, Saeed ; Aghababa, Mohammad Pourmahmood ; Heydari, Aghileh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-1c8ce8407898ea833fd047d61e9144736a06a62207c922dd1c7f28a067e5b93a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adaptive control</topic><topic>Artificial Intelligence</topic><topic>Calculus</topic><topic>Closed loops</topic><topic>Complex Systems</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Control methods</topic><topic>Control theory</topic><topic>Controllers</topic><topic>Design</topic><topic>Differential equations</topic><topic>Dynamical systems</topic><topic>Engineering</topic><topic>Equilibrium</topic><topic>Feedback control</topic><topic>Fractional calculus</topic><topic>Fuzzy control</topic><topic>Fuzzy logic</topic><topic>Mathematical models</topic><topic>Mechatronics</topic><topic>Neural networks</topic><topic>Nonlinear control</topic><topic>Nonlinear systems</topic><topic>Original Article</topic><topic>Parameters</topic><topic>Pattern Recognition</topic><topic>Robotics</topic><topic>Stabilization</topic><topic>System dynamics</topic><topic>Systems Biology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mirzajani, Saeed</creatorcontrib><creatorcontrib>Aghababa, Mohammad Pourmahmood</creatorcontrib><creatorcontrib>Heydari, Aghileh</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Database (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</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>Engineering Database</collection><collection>ProQuest advanced technologies & aerospace journals</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>Engineering collection</collection><jtitle>International journal of machine learning and cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mirzajani, Saeed</au><au>Aghababa, Mohammad Pourmahmood</au><au>Heydari, Aghileh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive control of nonlinear fractional-order systems using T–S fuzzy method</atitle><jtitle>International journal of machine learning and cybernetics</jtitle><stitle>Int. J. Mach. Learn. & Cyber</stitle><date>2019-03-01</date><risdate>2019</risdate><volume>10</volume><issue>3</issue><spage>527</spage><epage>540</epage><pages>527-540</pages><issn>1868-8071</issn><eissn>1868-808X</eissn><abstract>Owing to the superior capability of fractional differential equations in modeling and characterizing accurate dynamical properties of many high technology real world systems, the design and control of fractional-order systems have captured lots of attention in recent decades. In this paper, an adaptive intelligent fuzzy approach to controlling and stabilization of nonlinear non-autonomous fractional-order systems is proposed. Since dynamic equations of applied fractional-order systems usually contain various parameters and nonlinear terms, the Takagi–Sugeno (T–S) fuzzy models with if-then rules are adopted to describe the system dynamics. Also, as the nonlinear system parameters are assumed to be unknown, adaptive laws are derived to estimate such fluctuations. Simple adaptive linear-like control rules are developed based on the T–S fuzzy control theory. The stability of the resulting closed loop system is guaranteed by Lyapunov’s stability theory. Two illustrative numerical examples are presented to emphasize the correct performance and applicability of the proposed adaptive fuzzy control methodology. It is worth to notice that the proposed controller works well for stabilization of a wide class of either autonomous nonlinear uncertain fractional-order systems or non-autonomous complex systems with unknown parameters.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s13042-017-0733-1</doi><tpages>14</tpages></addata></record> |
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subjects | Adaptive control Artificial Intelligence Calculus Closed loops Complex Systems Computational Intelligence Control Control methods Control theory Controllers Design Differential equations Dynamical systems Engineering Equilibrium Feedback control Fractional calculus Fuzzy control Fuzzy logic Mathematical models Mechatronics Neural networks Nonlinear control Nonlinear systems Original Article Parameters Pattern Recognition Robotics Stabilization System dynamics Systems Biology |
title | Adaptive control of nonlinear fractional-order systems using T–S fuzzy method |
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