Synchronization of reaction–diffusion Hopfield neural networks with s-delays through sliding mode control
Articles
Xiao Liang
Shandong University of Science and Technology
https://orcid.org/0000-0002-1046-5472
Shuo Wang
Shandong University of Science and Technology
https://orcid.org/0000-0002-6263-3490
Ruili Wang
Institute of Applied Physics and Computational Mathematics
Xingzhi Hu
China Aerodynamics Research and Development Center
Zhen Wang
Shandong University of Science and Technology
https://orcid.org/0000-0002-1046-5472
Published 2022-03-01
https://doi.org/10.15388/namc.2022.27.25388
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Keywords

distributed system
sliding mode control
synchronization
Lyapunov–Krasovskii functional
s-delay

How to Cite

Liang X., Wang S., Wang R., Hu X. and Wang Z. (2022) “Synchronization of reaction–diffusion Hopfield neural networks with s-delays through sliding mode control”, Nonlinear Analysis: Modelling and Control, 27(2), pp. 331-349. doi: 10.15388/namc.2022.27.25388.

Abstract

Synchronization of reaction–diffusion Hopfield neural networks with s-delays via sliding mode control (SMC) is investigated in this paper. To begin with, the system is studied in an abstract Hilbert space C([–r; 0];U) rather than usual Euclid space Rn. Then we prove that the state vector of the drive system synchronizes to that of the response system on the switching surface, which relies on equivalent control. Furthermore, we prove that switching surface is the sliding mode area under SMC. Moreover, SMC controller can also force with any initial state to reach the switching surface within finite time, and the approximating time estimate is given explicitly. These criteria are easy to check and have less restrictions, so they can provide solid theoretical guidance for practical design in the future. Three different novel Lyapunov–Krasovskii functionals are used in corresponding proofs. Meanwhile, some inequalities such as Young inequality, Cauchy inequality, Poincaré inequality, Hanalay inequality are applied in these proofs. Finally, an example is given to illustrate the availability of our theoretical result, and the simulation is also carried out based on Runge–Kutta–Chebyshev method through Matlab.

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