Advanced Mathematical Methods for Networked System Control and Synchronization

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".

Deadline for manuscript submissions: 10 August 2025 | Viewed by 748

Special Issue Editors


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Guest Editor
Department of Engineering, State University of New York Maritime College, New York, NY 10465, USA
Interests: mathematic models; stochastic complex networks; brain-like computing

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Guest Editor
School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin 300387, China
Interests: complex dynamical networks; passivity and synchronization; stability theory and applications; analysis and design of algorithms; computer algebra

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Guest Editor
Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, School of Computer Science and Technology, Tiangong University, Tianjin 300387, China
Interests: complex networks; coupled neural networks; multiagent systems; fractional-order systems
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Special Issue Information

Dear Colleagues,

This Special Issue, titled "Advanced Mathematical Methods for Networked System Control and Synchronization", will investigate the latest mathematical techniques aimed at enhancing the control and synchronization of complex, interconnected systems. It covers a broad range of topics, ranging from stability and controllability in dynamic networks to synchronization across various systems, including cyber–physical and biological networks. Practical applications will also be explored in areas such as power grids, autonomous vehicles, communication networks, robotic swarms, sensor networks, and distributed computing systems, aiming to solve real-world challenges related to robustness, adaptability, and scalability. Contributions focus on mathematical tools like differential equations, Lyapunov functions, consensus algorithms, optimization algorithms, graph theory, game theory, and nonlinear control theory to ensure the efficient, reliable, and synchronized operation of interconnected systems across diverse domains.

Prof. Dr. Ziqian Liu
Dr. Yanli Huang
Prof. Dr. Jinliang Wang
Guest Editors

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Keywords

  • networked systems
  • control theory
  • synchronization
  • differential equations
  • graph theory
  • optimization algorithms
  • Lyapunov functions
  • consensus algorithms
  • game theory
  • Kalman filters

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Published Papers (1 paper)

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Research

48 pages, 7973 KiB  
Article
Synchronization Optimization Model Based on Enhanced Connectivity of New Energy Vehicle Supply Chain Network
by Haiwei Gao, Xiaomin Zhu, Binghui Guo, Yifan Cao, Haotian Wang, Xiaohan Yu and Xiaobo Yang
Mathematics 2025, 13(4), 632; https://doi.org/10.3390/math13040632 - 14 Feb 2025
Viewed by 546
Abstract
The synchronization of the new energy vehicle (NEV) supply chain network is crucial for enhancing industrial integration, building intelligent supply chain systems, and promoting sustainable development. This study proposes a novel synchronization model for the NEV supply chain network, incorporating a technical method [...] Read more.
The synchronization of the new energy vehicle (NEV) supply chain network is crucial for enhancing industrial integration, building intelligent supply chain systems, and promoting sustainable development. This study proposes a novel synchronization model for the NEV supply chain network, incorporating a technical method for measuring synchronization intervals. The research makes three key contributions: (1) development of a dynamic synchronization model capturing the complex interactions within NEV supply chains; (2) introduction of a quantitative method for assessing synchronization intervals; and (3) identification of critical parameters influencing network synchronization. Methodologically, we employ a combination of complex network theory and nonlinear dynamic systems to construct the synchronization model. The study utilizes real-world data from two major NEV companies (X and T) to validate the model’s effectiveness. Through network topology analysis and parameter optimization, we demonstrate significant improvements in supply chain efficiency and resilience. The practical application of this research lies in its ability to provide actionable insights for supply chain management. By optimizing network structure, coupling strength, and information delay, companies can enhance synchronization, reduce the bullwhip effect, and improve overall supply chain performance. The findings offer valuable guidance for NEV manufacturers and policymakers in building more resilient and efficient supply chain networks in the rapidly evolving automotive industry. Full article
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