Bipartite Synchronization for Signed Luré Networks via Semi-Markovian Jump Switching and Quantized Pinning Control

This paper investigates bipartite synchronization in signed Lur’e networks influenced by semi-Markovian jump dynamics. A control strategy is proposed that adapts to mode-dependent switching by combining quantized feedback with selective pinning. The approach accommodates both leaderless and leader–following synchronization scenarios. For each switching mode, Lyapunov–Krasovskii-based analysis is employed to establish sufficient conditions using linear matrix inequalities (LMIs). The robustness and convergence of the method are confirmed through simulation studies, even in the presence of stochastic switching and limited communication precision.

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Publication Details

Journal
Eng—Advances in Engineering
Published
2026-02-01
DOI
https://doi.org/10.3390/eng7020066
Primary Topic
Neural Networks Stability and Synchronization
Type
article
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article

Bipartite Synchronization for Signed Luré Networks via Semi-Markovian Jump Switching and Quantized Pinning Control

Suresh Rasappan, Sathish Kumar Kumaravel, Wardah Abdullah Al Majrafi, Pugalarasu Rajan et al.
Eng—Advances in Engineering
Neural Networks Stability and Synchronization
article

Bipartite Synchronization for Signed Luré Networks via Semi-Markovian Jump Switching and Quantized Pinning Control

Suresh Rasappan, Sathish Kumar Kumaravel, Wardah Abdullah Al Majrafi, Pugalarasu Rajan, Regan Murugesan
article en

Abstract

This paper investigates bipartite synchronization in signed Lur’e networks influenced by semi-Markovian jump dynamics. A control strategy is proposed that adapts to mode-dependent switching by combining quantized feedback with selective pinning. The approach accommodates both leaderless and leader–following synchronization scenarios. For each switching mode, Lyapunov–Krasovskii-based analysis is employed to establish sufficient conditions using linear matrix inequalities (LMIs). The robustness and convergence of the method are confirmed through simulation studies, even in the presence of stochastic switching and limited communication precision.

Eng—Advances in EngineeringVol. 7(2)
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (IN), College of Applied Sciences- Ibri (OM)
Reduced inequalities
Openalex Percentile: Top 75%
Neural Networks Stability and Synchronization
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