Adaptive fuzzy control for a class of unknown fractional-order neural networks subject to input nonlinearities and dead-zones
This paper presents an adaptive fuzzy control (AFC) for uncertain fractional-order neural networks (FONNs) with input nonlinearities and unmodeled dynamics. System uncertainties and unknown parts of the nonlinear input are approximated by fuzzy logic systems (FLSs). Based on some proposed stability...
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| Published in | Information sciences Vol. 454-455; pp. 30 - 45 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.07.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-0255 1872-6291 |
| DOI | 10.1016/j.ins.2018.04.069 |
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| Abstract | This paper presents an adaptive fuzzy control (AFC) for uncertain fractional-order neural networks (FONNs) with input nonlinearities and unmodeled dynamics. System uncertainties and unknown parts of the nonlinear input are approximated by fuzzy logic systems (FLSs). Based on some proposed stability analysis criteria for fractional-order systems (FOSs), an AFC is designed to guarantee the asymptotic stability of the controlled system. Fractional-order adaptive laws (FOALs) are constructed to update adjustable parameters of FLSs. Our method can be used to control FONNs with/without sector nonlinearities in control inputs. It also allows us to generalize many existing control methods that are valid for integer-order neural networks to FONNs by using the proposed method. Finally, the effectiveness of the proposed method is demonstrated by simulation results. |
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| AbstractList | This paper presents an adaptive fuzzy control (AFC) for uncertain fractional-order neural networks (FONNs) with input nonlinearities and unmodeled dynamics. System uncertainties and unknown parts of the nonlinear input are approximated by fuzzy logic systems (FLSs). Based on some proposed stability analysis criteria for fractional-order systems (FOSs), an AFC is designed to guarantee the asymptotic stability of the controlled system. Fractional-order adaptive laws (FOALs) are constructed to update adjustable parameters of FLSs. Our method can be used to control FONNs with/without sector nonlinearities in control inputs. It also allows us to generalize many existing control methods that are valid for integer-order neural networks to FONNs by using the proposed method. Finally, the effectiveness of the proposed method is demonstrated by simulation results. |
| Author | Li, Shenggang Liu, Heng Wang, Hongxing Sun, Yeguo |
| Author_xml | – sequence: 1 givenname: Heng orcidid: 0000-0003-3923-7526 surname: Liu fullname: Liu, Heng organization: College of Mathematics and Information Science, Shaanxi Normal Universtiy, Xi’an 710119, China – sequence: 2 givenname: Shenggang surname: Li fullname: Li, Shenggang email: shengganglinew@126.com, topologyli@126.com organization: College of Mathematics and Information Science, Shaanxi Normal Universtiy, Xi’an 710119, China – sequence: 3 givenname: Hongxing surname: Wang fullname: Wang, Hongxing organization: Department of Mathematics and Computational Science, Huainan Normal University, Huainan 232038, China – sequence: 4 givenname: Yeguo surname: Sun fullname: Sun, Yeguo organization: Department of Mathematics and Computational Science, Huainan Normal University, Huainan 232038, China |
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| Keywords | Adaptive fuzzy control Sector nonlinearity Dead-zone Fractional-order neural network |
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| Title | Adaptive fuzzy control for a class of unknown fractional-order neural networks subject to input nonlinearities and dead-zones |
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