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Design of an intelligent prediction-based neural network controller for multi-scroll chaotic systems

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Abstract

An indispensable part of the precise control of multi-scroll chaotic systems, model identification has received increasing attention in recent years. Because of plant uncertainty and unmodeled dynamics, conventional control methods cannot guarantee a sufficiently high-performance for stabilizing multi-scroll chaotic systems. In an effort to tackle the matter better, we propose an intelligent controller called the adaptive neural network prediction-based controller (NN-PbC ). The specified neural network is trained with the system model, which is extracted from a time series. In actual practice, the data are divided into two sets. One set is used for training and the other set for testing. In fact, a generalized NN will perform well for both training and testing data. The prediction-based control method is then applied to the obtained neural network model to stabilize the multiple equilibrium points. The stability of the closed-loop system is proven. In addition, simulation examples on two typical multi-scroll chaotic systems are presented to demonstrate the effectiveness of the proposed controller.

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Acknowledgments

The work described in this paper is supported by the DGRSDT (Direction Générale de la Recherche Scientifique et du Développement Technologique) of Algeria Grant N ∘ D01720130025. The authors also gratefully acknowledge the helpful comments and suggestions of the anonymous reviewers that have improved the quality of the paper.

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Authors and Affiliations

  1. Department of Electronics, Jijel University, BP 98 Ouled Aissa, Jijel, 18000, Algeria

    Mohammed Amin Khelifa & Abdelkrim Boukabou

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  1. Mohammed Amin Khelifa
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  2. Abdelkrim Boukabou
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Correspondence to Mohammed Amin Khelifa.

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Khelifa, M.A., Boukabou, A. Design of an intelligent prediction-based neural network controller for multi-scroll chaotic systems. Appl Intell 45, 793–807 (2016). https://doi.org/10.1007/s10489-016-0793-z

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