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

Yuan Li | Zendy; Hui Lv | Zendy; Dongxiu Jiao | Zendy

Open Access

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

Author(s) -

Yuan Li,

Hui Lv,

Dongxiu Jiao

Publication year - 2017

Publication title -

aip advances

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.421

H-Index - 58

ISSN - 2158-3226

DOI - 10.1063/1.4978393

Subject(s) - control theory (sociology) , synchronization (alternating current) , controller (irrigation) , artificial neural network , adaptive control , convergence (economics) , nonlinear system , synchronization of chaos , computer science , lyapunov stability , chaotic , lyapunov function , integer (computer science) , mathematics , topology (electrical circuits) , control (management) , artificial intelligence , physics , combinatorics , quantum mechanics , agronomy , economics , biology , programming language , economic growth

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

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