Synchronization of chaotic Gyro systems via sliding mode control with cooperative weights neural network

Yanqiu Che, Ting Ting Yang, Xiao Qin Li, Rui Xue Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a sliding mode control (SMC) with a cooperative weights neural network (CWNN) is proposed to realize the synchronization of two chaotic Gyro systems with nonlinear uncertainties and external disturbances. By the Lyapunov stability method, the overall closed-loop system is shown to be stable and chaos synchronizationis obtained. The simulation results demonstrate the effectiveness of the proposed control method.

Original languageEnglish (US)
Title of host publicationMechanical Engineering, Industrial Electronics and Information Technology Applications in Industry
Pages1101-1104
Number of pages4
DOIs
StatePublished - Oct 30 2013
Event2nd International Conference on Mechanical Engineering, Industrial Electronics and Informatization, MEIEI 2013 - Chongqing, China
Duration: Sep 14 2013Sep 15 2013

Publication series

NameApplied Mechanics and Materials
Volume427-429
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Other

Other2nd International Conference on Mechanical Engineering, Industrial Electronics and Informatization, MEIEI 2013
CountryChina
CityChongqing
Period9/14/139/15/13

Fingerprint

Chaotic systems
Sliding mode control
Closed loop systems
Chaos theory
Synchronization
Neural networks
Uncertainty

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Che, Y., Yang, T. T., Li, X. Q., & Li, R. X. (2013). Synchronization of chaotic Gyro systems via sliding mode control with cooperative weights neural network. In Mechanical Engineering, Industrial Electronics and Information Technology Applications in Industry (pp. 1101-1104). (Applied Mechanics and Materials; Vol. 427-429). https://doi.org/10.4028/www.scientific.net/AMM.427-429.1101
Che, Yanqiu ; Yang, Ting Ting ; Li, Xiao Qin ; Li, Rui Xue. / Synchronization of chaotic Gyro systems via sliding mode control with cooperative weights neural network. Mechanical Engineering, Industrial Electronics and Information Technology Applications in Industry. 2013. pp. 1101-1104 (Applied Mechanics and Materials).
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abstract = "In this paper, a sliding mode control (SMC) with a cooperative weights neural network (CWNN) is proposed to realize the synchronization of two chaotic Gyro systems with nonlinear uncertainties and external disturbances. By the Lyapunov stability method, the overall closed-loop system is shown to be stable and chaos synchronizationis obtained. The simulation results demonstrate the effectiveness of the proposed control method.",
author = "Yanqiu Che and Yang, {Ting Ting} and Li, {Xiao Qin} and Li, {Rui Xue}",
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Che, Y, Yang, TT, Li, XQ & Li, RX 2013, Synchronization of chaotic Gyro systems via sliding mode control with cooperative weights neural network. in Mechanical Engineering, Industrial Electronics and Information Technology Applications in Industry. Applied Mechanics and Materials, vol. 427-429, pp. 1101-1104, 2nd International Conference on Mechanical Engineering, Industrial Electronics and Informatization, MEIEI 2013, Chongqing, China, 9/14/13. https://doi.org/10.4028/www.scientific.net/AMM.427-429.1101

Synchronization of chaotic Gyro systems via sliding mode control with cooperative weights neural network. / Che, Yanqiu; Yang, Ting Ting; Li, Xiao Qin; Li, Rui Xue.

Mechanical Engineering, Industrial Electronics and Information Technology Applications in Industry. 2013. p. 1101-1104 (Applied Mechanics and Materials; Vol. 427-429).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - In this paper, a sliding mode control (SMC) with a cooperative weights neural network (CWNN) is proposed to realize the synchronization of two chaotic Gyro systems with nonlinear uncertainties and external disturbances. By the Lyapunov stability method, the overall closed-loop system is shown to be stable and chaos synchronizationis obtained. The simulation results demonstrate the effectiveness of the proposed control method.

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Che Y, Yang TT, Li XQ, Li RX. Synchronization of chaotic Gyro systems via sliding mode control with cooperative weights neural network. In Mechanical Engineering, Industrial Electronics and Information Technology Applications in Industry. 2013. p. 1101-1104. (Applied Mechanics and Materials). https://doi.org/10.4028/www.scientific.net/AMM.427-429.1101