Adaptive Neural Network Synchronization Control for Uncertain Fractional-Order Time-Delay Chaotic Systems
We propose an adaptive radial basis (RBF) neural network controller based on Lyapunov stability theory for uncertain fractional-order time-delay chaotic systems (FOTDCSs) with different time delays. The controller does not depend on the system model and can achieve synchronous control under the cond...
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Format: | Article |
Language: | English |
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MDPI AG
2023-03-01
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Series: | Fractal and Fractional |
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Online Access: | https://www.mdpi.com/2504-3110/7/4/288 |
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author | Wenhao Yan Zijing Jiang Xin Huang Qun Ding |
author_facet | Wenhao Yan Zijing Jiang Xin Huang Qun Ding |
author_sort | Wenhao Yan |
collection | DOAJ |
description | We propose an adaptive radial basis (RBF) neural network controller based on Lyapunov stability theory for uncertain fractional-order time-delay chaotic systems (FOTDCSs) with different time delays. The controller does not depend on the system model and can achieve synchronous control under the condition that nonlinear uncertainties and external disturbances are completely unknown. Stability analysis showed that the error system asymptotically tended to zero in combination with the relevant lemma. Numerical simulation results show the effectiveness of the controller. |
first_indexed | 2024-03-11T05:00:27Z |
format | Article |
id | doaj.art-c0229204471842068c5ae2695e8e001c |
institution | Directory Open Access Journal |
issn | 2504-3110 |
language | English |
last_indexed | 2024-03-11T05:00:27Z |
publishDate | 2023-03-01 |
publisher | MDPI AG |
record_format | Article |
series | Fractal and Fractional |
spelling | doaj.art-c0229204471842068c5ae2695e8e001c2023-11-17T19:19:03ZengMDPI AGFractal and Fractional2504-31102023-03-017428810.3390/fractalfract7040288Adaptive Neural Network Synchronization Control for Uncertain Fractional-Order Time-Delay Chaotic SystemsWenhao Yan0Zijing Jiang1Xin Huang2Qun Ding3Electronic Engineering College, Heilongjiang University, Harbin 150080, ChinaElectronic Engineering College, Heilongjiang University, Harbin 150080, ChinaElectronic Engineering College, Heilongjiang University, Harbin 150080, ChinaElectronic Engineering College, Heilongjiang University, Harbin 150080, ChinaWe propose an adaptive radial basis (RBF) neural network controller based on Lyapunov stability theory for uncertain fractional-order time-delay chaotic systems (FOTDCSs) with different time delays. The controller does not depend on the system model and can achieve synchronous control under the condition that nonlinear uncertainties and external disturbances are completely unknown. Stability analysis showed that the error system asymptotically tended to zero in combination with the relevant lemma. Numerical simulation results show the effectiveness of the controller.https://www.mdpi.com/2504-3110/7/4/288radial basis neural networkLyapunov stability theoryfractional-order time-delay chaotic system |
spellingShingle | Wenhao Yan Zijing Jiang Xin Huang Qun Ding Adaptive Neural Network Synchronization Control for Uncertain Fractional-Order Time-Delay Chaotic Systems Fractal and Fractional radial basis neural network Lyapunov stability theory fractional-order time-delay chaotic system |
title | Adaptive Neural Network Synchronization Control for Uncertain Fractional-Order Time-Delay Chaotic Systems |
title_full | Adaptive Neural Network Synchronization Control for Uncertain Fractional-Order Time-Delay Chaotic Systems |
title_fullStr | Adaptive Neural Network Synchronization Control for Uncertain Fractional-Order Time-Delay Chaotic Systems |
title_full_unstemmed | Adaptive Neural Network Synchronization Control for Uncertain Fractional-Order Time-Delay Chaotic Systems |
title_short | Adaptive Neural Network Synchronization Control for Uncertain Fractional-Order Time-Delay Chaotic Systems |
title_sort | adaptive neural network synchronization control for uncertain fractional order time delay chaotic systems |
topic | radial basis neural network Lyapunov stability theory fractional-order time-delay chaotic system |
url | https://www.mdpi.com/2504-3110/7/4/288 |
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