Adaptive Sliding Mode Synchronous Control for Complex Networks With Amplify-and-Forward Relays

This paper aims to the remote synchronous control for complex networks with amplify-and-forward (AF) relays. Firstly, the AF relay dynamic with random noise is integrated into the control signal transmission. Secondly, the complex networks incorporating an AF relay is converted into the sliding control mode (SMC), which robustly addresses unknown disturbances among the nodes to achieve synchronous states. Thirdly, an adaptive gain mechanism based on the equivalent value of sign function of SMC is proposed to estimate the disturbance amplitude, hence mitigating the chattering of SMC. Finally, the illustrative simulation demonstrated the effectiveness of proposed methodology.

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Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tase.2025.3578652
Citations
1
Primary Topic
Neural Networks Stability and Synchronization
Type
article
Field-Weighted Citation Impact
1.49

Funders

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article

Adaptive Sliding Mode Synchronous Control for Complex Networks With Amplify-and-Forward Relays

Wei Qian, Yi Yang, Tian Wang, Keping Wang
1 citations
IEEE Transactions on Automation Science and Engineering
Neural Networks Stability and Synchronization
1.49
article

Adaptive Sliding Mode Synchronous Control for Complex Networks With Amplify-and-Forward Relays

Wei Qian, Yi Yang, Tian Wang, Keping Wang
article en
1 citations

Abstract

This paper aims to the remote synchronous control for complex networks with amplify-and-forward (AF) relays. Firstly, the AF relay dynamic with random noise is integrated into the control signal transmission. Secondly, the complex networks incorporating an AF relay is converted into the sliding control mode (SMC), which robustly addresses unknown disturbances among the nodes to achieve synchronous states. Thirdly, an adaptive gain mechanism based on the equivalent value of sign function of SMC is proposed to estimate the disturbance amplitude, hence mitigating the chattering of SMC. Finally, the illustrative simulation demonstrated the effectiveness of proposed methodology.

IEEE Transactions on Automation Science and EngineeringVol. 22
Henan Polytechnic University (CN), Beihang University (CN)
National Natural Science Foundation of China, Beijing Nova Program
Affordable and clean energy
Openalex Percentile: Top 16%
Neural Networks Stability and Synchronization
1.49
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