Finite-time sliding mode synchronisation of a fractional-order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks

被引:0
|
作者
Shao, Keyong [1 ]
Feng, Ao [1 ]
Wang, Tingting [1 ]
Li, Wenju [1 ]
Jiang, Jilu [1 ]
机构
[1] Northeast Petr Univ, Sch Elect Informat & Engn, Daqing, Peoples R China
基金
中国国家自然科学基金;
关键词
differential evolution algorithm; finite-time sliding mode controller; fractional hyperchaotic system; radial basis function neural network (RBFNN); recurrent neural network (RNN); PARTICLE SWARM OPTIMIZATION;
D O I
10.1049/ntw2.12069
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:87 / 97
页数:11
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