Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach

In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region...

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Main Authors: Yuan Li, Hui Lv, Dongxiu Jiao
Format: Article
Language:English
Published: AIP Publishing LLC 2017-03-01
Series:AIP Advances
Online Access:http://dx.doi.org/10.1063/1.4978393
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spelling doaj-7daa31948df24f15a863f2601d4a63012020-11-24T23:17:12ZengAIP Publishing LLCAIP Advances2158-32262017-03-0173035106035106-1210.1063/1.4978393022703ADVPrescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approachYuan Li0Hui Lv1Dongxiu Jiao2Department of Media Management, Communication University of Shanxi, Jinzhong 030619, ChinaDepartment of Applied Mathematics, Huainan Normal University, Huainan 232038, ChinaDepartment of Media Management, Communication University of Shanxi, Jinzhong 030619, ChinaIn this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.http://dx.doi.org/10.1063/1.4978393
collection DOAJ
language English
format Article
sources DOAJ
author Yuan Li
Hui Lv
Dongxiu Jiao
spellingShingle Yuan Li
Hui Lv
Dongxiu Jiao
Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach
AIP Advances
author_facet Yuan Li
Hui Lv
Dongxiu Jiao
author_sort Yuan Li
title Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach
title_short Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach
title_full Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach
title_fullStr Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach
title_full_unstemmed Prescribed performance synchronization controller design of fractional-order chaotic systems: An adaptive neural network control approach
title_sort prescribed performance synchronization controller design of fractional-order chaotic systems: an adaptive neural network control approach
publisher AIP Publishing LLC
series AIP Advances
issn 2158-3226
publishDate 2017-03-01
description In this study, an adaptive neural network synchronization (NNS) approach, capable of guaranteeing prescribed performance (PP), is designed for non-identical fractional-order chaotic systems (FOCSs). For PP synchronization, we mean that the synchronization error converges to an arbitrary small region of the origin with convergence rate greater than some function given in advance. Neural networks are utilized to estimate unknown nonlinear functions in the closed-loop system. Based on the integer-order Lyapunov stability theorem, a fractional-order adaptive NNS controller is designed, and the PP can be guaranteed. Finally, simulation results are presented to confirm our results.
url http://dx.doi.org/10.1063/1.4978393
work_keys_str_mv AT yuanli prescribedperformancesynchronizationcontrollerdesignoffractionalorderchaoticsystemsanadaptiveneuralnetworkcontrolapproach
AT huilv prescribedperformancesynchronizationcontrollerdesignoffractionalorderchaoticsystemsanadaptiveneuralnetworkcontrolapproach
AT dongxiujiao prescribedperformancesynchronizationcontrollerdesignoffractionalorderchaoticsystemsanadaptiveneuralnetworkcontrolapproach
_version_ 1725584300203573248

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