Applied Intelligence

, Volume 45, Issue 3, pp 793–807 | Cite as

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

Article
First Online:

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.

Keywords

Adaptive neural network Multi-scroll chaotic systems Prediction-based control System identification 
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Notes

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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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of ElectronicsJijel UniversityJijelAlgeria

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