Research on Synchronization Control of Complex Systems with Unknown Disturbances

Synchronization behavior is one of the most important characteristics of system. However, in practice, it is impossible to know all the system parameters, which increases the difficulty of network synchronization control. Thus, a controller is designed to achieve network synchronization based on the pinning control method, the extended state observer theory and the backstepping sliding mode control method. First, an extended state observer is designed to observe the uncertain variables. Second, a pinning controller is designed to realize network synchronization based on the backstepping sliding mode control. Finally, the simulation of a random 30-node Lorenz chaotic network verifies the correctness of this method.

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Publication Details

Published
2024-11-06
DOI
https://doi.org/10.1109/ciycee63099.2024.10846224
Citations
1
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
0.35
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Research on Synchronization Control of Complex Systems with Unknown Disturbances

Zhongjian Kang, Zheng Chang, Kun Xue, Hongguo Yu et al.
1 citations
Neural Networks Stability and Synchronization
0.35
article

Research on Synchronization Control of Complex Systems with Unknown Disturbances

Zhongjian Kang, Zheng Chang, Kun Xue, Hongguo Yu, Changchao Li, Hao Wang
article en
1 citations

Abstract

Synchronization behavior is one of the most important characteristics of system. However, in practice, it is impossible to know all the system parameters, which increases the difficulty of network synchronization control. Thus, a controller is designed to achieve network synchronization based on the pinning control method, the extended state observer theory and the backstepping sliding mode control method. First, an extended state observer is designed to observe the uncertain variables. Second, a pinning controller is designed to realize network synchronization based on the backstepping sliding mode control. Finally, the simulation of a random 30-node Lorenz chaotic network verifies the correctness of this method.

Shandong University (CN), Haier Group (China) (CN), China University of Petroleum, East China (CN), Shandong University of Science and Technology (CN)
Climate action
Openalex Percentile: Top 34%
Neural Networks Stability and Synchronization
0.35
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