Title: Synchronisation of chaotic systems using neural generalised predictive control

Authors: Zakaria Driss; Noura Mansouri

Addresses: Department of Electrical Engineering, University of Constantine 1 UMC, Algeria ' Department of Electrical Engineering, University of Constantine 1 UMC, Algeria

Abstract: In this paper, a successful implementation of a neural generalised predictive control (NGPC) method for synchronisation of uncertain chaotic and hyperchaotic systems is presented. For this purpose, multi-layer feedforward neural network and particle swarm optimisation method (PSO) are used as system's model and optimisation algorithm, respectively. The synchronisation of two 3D Lorenz systems and 4D Lü hyperchaotic systems is investigated using the proposed method in different situations: complete synchronisation, hybrid synchronisation, and synchronisation based on one control input. Simulation results show satisfying performance of the proposed implementation in terms of the quality of the control input and the ability to solve many problems with only slight adaptations.

Keywords: chaos theory; synchronisation; generalised predictive control; neural network; particle swarm optimisation; PSO.

DOI: 10.1504/IJAAC.2020.108272

International Journal of Automation and Control, 2020 Vol.14 No.4, pp.377 - 398

Received: 05 Sep 2018
Accepted: 04 Dec 2018
Published online: 08 Jul 2020 *

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