A simplified adaptive interval Type-2 fuzzy control in practical industrial application
Adaptive Type-2 fuzzy control possesses control performance better than the traditional adaptive fuzzy control. However, heavy computation burden obviously blocks the utilization of adaptive Type-2 fuzzy control in industrial application. By adopting novel piecewise fuzzy sets and center-average typ...
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| Published in | Journal of Central South University Vol. 21; no. 7; pp. 2693 - 2700 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Heidelberg
Central South University
01.07.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2095-2899 2227-5223 |
| DOI | 10.1007/s11771-014-2231-x |
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| Abstract | Adaptive Type-2 fuzzy control possesses control performance better than the traditional adaptive fuzzy control. However, heavy computation burden obviously blocks the utilization of adaptive Type-2 fuzzy control in industrial application. By adopting novel piecewise fuzzy sets and center-average type-reduction, a simplified adaptive interval Type-2 fuzzy controller involving less computation is developed for practical industrial application. In the proposed controller, the inputs are divided into several subintervals and then two piecewise fuzzy sets are used for each subinterval. With the manner of piecewise fuzzy sets and a novel fuzzy rules inference engine, only part of fuzzy rules are simultaneously activated in one control loop, which exponentially decreases the computation and makes the controller appropriate in industrial application. The simulation and experimental study, involving the popular magnetic levitation platform, shows the predicted system with theoretical stability and good tracking performance. The analysis indicates that there is far less computation of the proposed controller than the traditional adaptive interval Type-2 fuzzy controller, especially when the number of fuzzy rules and fuzzy sets is large, and the controller still maintains good control performance as the traditional one. |
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| AbstractList | Adaptive Type-2 fuzzy control possesses control performance better than the traditional adaptive fuzzy control. However, heavy computation burden obviously blocks the utilization of adaptive Type-2 fuzzy control in industrial application. By adopting novel piecewise fuzzy sets and center-average type-reduction, a simplified adaptive interval Type-2 fuzzy controller involving less computation is developed for practical industrial application. In the proposed controller, the inputs are divided into several subintervals and then two piecewise fuzzy sets are used for each subinterval. With the manner of piecewise fuzzy sets and a novel fuzzy rules inference engine, only part of fuzzy rules are simultaneously activated in one control loop, which exponentially decreases the computation and makes the controller appropriate in industrial application. The simulation and experimental study, involving the popular magnetic levitation platform, shows the predicted system with theoretical stability and good tracking performance. The analysis indicates that there is far less computation of the proposed controller than the traditional adaptive interval Type-2 fuzzy controller, especially when the number of fuzzy rules and fuzzy sets is large, and the controller still maintains good control performance as the traditional one. |
| Author | Zhou, Hai-bo Duan, Ji-an Zhou, Zhen-yu |
| Author_xml | – sequence: 1 givenname: Hai-bo surname: Zhou fullname: Zhou, Hai-bo organization: State Key Laboratory of High Performance Complex Manufacturing (Central South University) – sequence: 2 givenname: Ji-an surname: Duan fullname: Duan, Ji-an email: duanjian@csu.edu.cn organization: State Key Laboratory of High Performance Complex Manufacturing (Central South University) – sequence: 3 givenname: Zhen-yu surname: Zhou fullname: Zhou, Zhen-yu organization: State Key Laboratory of High Performance Complex Manufacturing (Central South University) |
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| CitedBy_id | crossref_primary_10_1049_iet_cta_2017_0610 crossref_primary_10_1007_s00500_018_3367_7 crossref_primary_10_1109_TCYB_2016_2570239 crossref_primary_10_1016_j_eswa_2023_121682 crossref_primary_10_3233_IFS_151951 |
| Cites_doi | 10.1109/MCI.2007.357194 10.1109/TMAG.2010.2087388 10.1007/s11771-011-0760-0 10.1109/TCST.2008.2002755 10.1109/TFUZZ.2012.2226891 10.1007/s11771-010-0624-z 10.1016/j.conengprac.2011.08.006 10.1109/TFUZZ.2006.882463 10.1016/S0005-1098(99)00040-0 10.1109/TFUZZ.2008.924329 10.1109/TFUZZ.2006.877355 10.1109/TIE.2003.812354 10.1109/TFUZZ.2002.806314 10.1109/MCI.2007.357192 10.1109/91.811231 10.1109/3516.990889 10.1109/TNN.2007.900814 10.1109/TFUZZ.2010.2049022 10.1109/TFUZZ.2002.803496 10.1016/j.ces.2010.04.023 |
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| Keywords | adaptive fuzzy control center-average type-reduction piecewise fuzzy set Type-2 fuzzy control |
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KPechevA NModel reference adaptive control of a maglev system with stable maximum descent criterion [J]Automatica19993581457146510.1016/S0005-1098(99)00040-01831486 – reference: WuD RMendelJ MEnhanced karnik-mendel algorithms [J]IEEE Transactions on Fuzzy Systems200917492393410.1109/TFUZZ.2008.924329 – reference: ShanX MKuoS KZhangJ HMenqC HUltra precision motion control of a multiple degrees of freedom magnetic suspension stage [J]IEEE-ASME Transactions on Mechatronics200271677710.1109/3516.990889 – reference: ZhouH BDuanJ ALevitation mechanism modelling for maglev transportation system [J]Journal of Central South University of Technology20101761230123710.1007/s11771-010-0624-z – reference: HsiaoM-yChenC-yLiT-hsengInterval Type-2 adaptive fuzzy sliding-mode dynamic control design for wheeled mobile robots [J]International Journal of Fuzzy Systems20081042682752569091 – reference: CaoJ-tJiX-fLiPLiuH-haiDesign of adaptive interval type-2 fuzzy control system and its stability analysis [J]International Journal of Fuzzy Systems20111343353432903556 – reference: ChenM YWangM JFuL CModeling and controller design of a maglev guiding system for application in precision positioning [J]IEEE Transactions on Industrial Electronics200350349350610.1109/TIE.2003.812354 – reference: DuX-yYingHaoDerivation and analysis of the analytical structures of the interval Type-2 fuzzy-PI and PD controllers [J]IEEE Transactions on Fuzzy Systems201018480281410.1109/TFUZZ.2010.2049022 – reference: JohnR ICouplandSType-2 fuzzy logic: A historical view [J]IEEE Computational Intelligence Magazine200721576210.1109/MCI.2007.357194 – reference: HagrasHType-2 FLCs: A new generation of fuzzy controllers [J]IEEE Computational Intelligence Magazine200721304310.1109/MCI.2007.357192 – reference: ZhouH-bYingHDuanJ-anAdaptive Type-2 fuzzy control for uncertain nonlinear system [J]Journal of Central South University of Technology20111876076610.1007/s11771-011-0760-0 – reference: YingHDeriving analytical input-output relationship for fuzzy controllers using arbitrary input fuzzy sets and Zadeh fuzzy and operator [J]IEEE Transactions on Fuzzy Systems200614565466210.1109/TFUZZ.2006.877355 – reference: DuanJ AZhouH BGuoN PElectromagnetic design of a novel linear maglev transportation platform with finite-element analysis [J]IEEE Transactions on Magnetics201147126026310.1109/TMAG.2010.2087388 – reference: FulfordCMaggioreMApkarianJControl of a 5DOF magnetically levitated positioning stage [J]IEEE Transactions on Control Systems Technology200917484485210.1109/TCST.2008.2002755 – reference: YingHGeneral interval type-2 mamdani fuzzy systems are universal approximators [C]2008 Annual Meeting of the North American Fuzzy Information Processing Society2008New YorkIEEE294299 – reference: KarnikN NMendelJ MLiangQ LType-2 fuzzy logic systems [J]IEEE Transactions on Fuzzy Systems19997664365810.1109/91.811231 – reference: JingM-qLiuHLianJ-xYuLieTwo-dimensional high precision positioning maglev stage [J]Journal of Xi’an Jiaotong University2008421113771380 – reference: MendelJ MUncertain rule-based fuzzy logic systems: introduction and new directions [M]2001Upper Saddle River, NJPrentice-Hall10120 – reference: YingHInterval Type-2 takagi-sugeno fuzzy systems with linear rule consequent are universal approximators [C]2009 Annual Meeting of the North American Fuzzy Information Processing Society2009Cincinnati, USAIEEE541545 – reference: WaiR JLeeJ DAdaptive fuzzy-neural-network control for maglev transportation system [J]IEEE Transactions on Neural Networks200819154702384756 – reference: WuH WMendelJ MUncertainty bounds and their use in the design of interval Type-2 fuzzy logic systems [J]IEEE Transactions on Fuzzy Systems200210562263910.1109/TFUZZ.2002.803496 – volume: 2 start-page: 57 issue: 1 year: 2007 ident: 2231_CR2 publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2007.357194 – volume: 47 start-page: 260 issue: 1 year: 2011 ident: 2231_CR26 publication-title: IEEE Transactions on Magnetics doi: 10.1109/TMAG.2010.2087388 – volume: 18 start-page: 760 year: 2011 ident: 2231_CR7 publication-title: Journal of Central South University of Technology doi: 10.1007/s11771-011-0760-0 – volume: 17 start-page: 844 issue: 4 year: 2009 ident: 2231_CR19 publication-title: IEEE Transactions on Control Systems Technology doi: 10.1109/TCST.2008.2002755 – volume: 21 start-page: 447 issue: 3 year: 2013 ident: 2231_CR11 publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2012.2226891 – volume: 17 start-page: 1230 issue: 6 year: 2010 ident: 2231_CR23 publication-title: Journal of Central South University of Technology doi: 10.1007/s11771-010-0624-z – volume: 19 start-page: 1468 issue: 12 year: 2011 ident: 2231_CR24 publication-title: Control Engineering Practice doi: 10.1016/j.conengprac.2011.08.006 – volume: 15 start-page: 309 issue: 2 year: 2007 ident: 2231_CR17 publication-title: IEEE Transactions on Fuzzy Systems doi: 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Jiaotong University – volume: 2 start-page: 30 issue: 1 year: 2007 ident: 2231_CR3 publication-title: IEEE Computational Intelligence Magazine doi: 10.1109/MCI.2007.357192 – volume: 7 start-page: 643 issue: 6 year: 1999 ident: 2231_CR15 publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/91.811231 – volume: 7 start-page: 67 issue: 1 year: 2002 ident: 2231_CR21 publication-title: IEEE-ASME Transactions on Mechatronics doi: 10.1109/3516.990889 – volume: 10 start-page: 268 issue: 4 year: 2008 ident: 2231_CR6 publication-title: International Journal of Fuzzy Systems – volume: 19 start-page: 54 issue: 1 year: 2008 ident: 2231_CR22 publication-title: IEEE Transactions on Neural Networks doi: 10.1109/TNN.2007.900814 – volume: 13 start-page: 335 issue: 4 year: 2011 ident: 2231_CR4 publication-title: International Journal of Fuzzy Systems – volume: 18 start-page: 802 issue: 4 year: 2010 ident: 2231_CR12 publication-title: IEEE Transactions on Fuzzy Systems doi: 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