Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks
Abstract To solve the synchronisation problem associated with fractional‐order hyperchaotic systems, in this study, a new dual‐neural network finite‐time sliding mode control method was developed, and a differential evolution algorithm was used to optimise the switching gain, control parameters, and...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
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Wiley
2023-05-01
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Series: | IET Networks |
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Online Access: | https://doi.org/10.1049/ntw2.12069 |
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author | Keyong Shao Ao Feng Tingting Wang Wenju Li Jilu Jiang |
author_facet | Keyong Shao Ao Feng Tingting Wang Wenju Li Jilu Jiang |
author_sort | Keyong Shao |
collection | DOAJ |
description | Abstract To solve the synchronisation problem associated with fractional‐order hyperchaotic systems, in this study, a new dual‐neural network finite‐time sliding mode control method was developed, and a differential evolution algorithm was used to optimise the switching gain, control parameters, and sliding mode surface parameters, greatly reducing chattering problems in sliding mode controllers. By using the developed method, the complete synchronisation of the drive system and the response system of a fractional‐order hyperchaotic system was realised in a finite time; moreover, the stability of the error system under this method was proved by using Lyapunov stability theorem. Numerical simulation results verified the feasibility and superiority of the method. |
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format | Article |
id | doaj.art-077b4aa3c94844bfbf2652a2fbc9e46a |
institution | Directory Open Access Journal |
issn | 2047-4954 2047-4962 |
language | English |
last_indexed | 2024-04-09T12:58:52Z |
publishDate | 2023-05-01 |
publisher | Wiley |
record_format | Article |
series | IET Networks |
spelling | doaj.art-077b4aa3c94844bfbf2652a2fbc9e46a2023-05-13T04:59:21ZengWileyIET Networks2047-49542047-49622023-05-01123879710.1049/ntw2.12069Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networksKeyong Shao0Ao Feng1Tingting Wang2Wenju Li3Jilu Jiang4School of Electrical Information&Engineering Northeast Petroleum University Daqing ChinaSchool of Electrical Information&Engineering Northeast Petroleum University Daqing ChinaSchool of Electrical Information&Engineering Northeast Petroleum University Daqing ChinaSchool of Electrical Information&Engineering Northeast Petroleum University Daqing ChinaSchool of Electrical Information&Engineering Northeast Petroleum University Daqing ChinaAbstract To solve the synchronisation problem associated with fractional‐order hyperchaotic systems, in this study, a new dual‐neural network finite‐time sliding mode control method was developed, and a differential evolution algorithm was used to optimise the switching gain, control parameters, and sliding mode surface parameters, greatly reducing chattering problems in sliding mode controllers. By using the developed method, the complete synchronisation of the drive system and the response system of a fractional‐order hyperchaotic system was realised in a finite time; moreover, the stability of the error system under this method was proved by using Lyapunov stability theorem. Numerical simulation results verified the feasibility and superiority of the method.https://doi.org/10.1049/ntw2.12069differential evolution algorithmfinite‐time sliding mode controllerfractional hyperchaotic systemradial basis function neural network (RBFNN)recurrent neural network (RNN) |
spellingShingle | Keyong Shao Ao Feng Tingting Wang Wenju Li Jilu Jiang Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks IET Networks differential evolution algorithm finite‐time sliding mode controller fractional hyperchaotic system radial basis function neural network (RBFNN) recurrent neural network (RNN) |
title | Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks |
title_full | Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks |
title_fullStr | Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks |
title_full_unstemmed | Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks |
title_short | Finite‐time sliding mode synchronisation of a fractional‐order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks |
title_sort | finite time sliding mode synchronisation of a fractional order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks |
topic | differential evolution algorithm finite‐time sliding mode controller fractional hyperchaotic system radial basis function neural network (RBFNN) recurrent neural network (RNN) |
url | https://doi.org/10.1049/ntw2.12069 |
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