Command filtered adaptive neural network synchronization control of fractional-order chaotic systems subject to unknown dead zones
Command filters are essential for alleviating the inherent computational complexity (ICC) of the standard backstepping control method. This paper addresses the synchronization control scheme for an uncertain fractional-order chaotic system (FOCS) subject to unknown dead zone input (DZI) based on a f...
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| Published in | Journal of the Franklin Institute Vol. 358; no. 7; pp. 3376 - 3402 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elmsford
Elsevier Ltd
01.05.2021
Elsevier Science Ltd |
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| Abstract | Command filters are essential for alleviating the inherent computational complexity (ICC) of the standard backstepping control method. This paper addresses the synchronization control scheme for an uncertain fractional-order chaotic system (FOCS) subject to unknown dead zone input (DZI) based on a fractional-order command filter (FCF). A virtual control function (VCF) and its fractional-order derivative are approximated by the output of the FCF. In order to handle filtering errors and obtain good control performance, an error compensation mechanism (ECM) is developed. A radial basis function neural network (RBFNN) is introduced to relax the requirement of the uncertain function must be linear in the standard backstepping control method. The construction of a VCF in each step satisfies the Lyapunov function to ensure the stability of the corresponding subsystem. By using the bounded information to cope with the unknown DZI, the stability of the synchronization error system is guaranteed. Finally, simulation results verify the effectiveness of our methods. |
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| AbstractList | Command filters are essential for alleviating the inherent computational complexity (ICC) of the standard backstepping control method. This paper addresses the synchronization control scheme for an uncertain fractional-order chaotic system (FOCS) subject to unknown dead zone input (DZI) based on a fractional-order command filter (FCF). A virtual control function (VCF) and its fractional-order derivative are approximated by the output of the FCF. In order to handle filtering errors and obtain good control performance, an error compensation mechanism (ECM) is developed. A radial basis function neural network (RBFNN) is introduced to relax the requirement of the uncertain function must be linear in the standard backstepping control method. The construction of a VCF in each step satisfies the Lyapunov function to ensure the stability of the corresponding subsystem. By using the bounded information to cope with the unknown DZI, the stability of the synchronization error system is guaranteed. Finally, simulation results verify the effectiveness of our methods. |
| Author | Ha, Shumin Chen, Liangyun Liu, Heng |
| Author_xml | – sequence: 1 givenname: Shumin surname: Ha fullname: Ha, Shumin organization: School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China – sequence: 2 givenname: Liangyun surname: Chen fullname: Chen, Liangyun organization: School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China – sequence: 3 givenname: Heng surname: Liu fullname: Liu, Heng email: liuheng122@gmail.com organization: School of Mathematics and Physics, Guangxi University for Nationalities, Nanning 530006, China |
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| Snippet | Command filters are essential for alleviating the inherent computational complexity (ICC) of the standard backstepping control method. This paper addresses the... |
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| SubjectTerms | Adaptive control Chaos theory Control methods Error compensation Fractions Liapunov functions Mathematical functions Neural networks Nonlinear equations Radial basis function Stability Subsystems Synchronism Systems stability |
| Title | Command filtered adaptive neural network synchronization control of fractional-order chaotic systems subject to unknown dead zones |
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