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

Abstract

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.

Keywords

• differential evolution algorithm
• finite‐time sliding mode controller
• fractional hyperchaotic system
• radial basis function neural network (RBFNN)
• recurrent neural network (RNN)