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Toward the minimum vertex cover of complex networks using distributed potential games

Science China(Information Sciences) 2023年第1期66卷 244-263页

作者： Jie CHEN Xiang LI Adaptive Networks and Control Lab Department of Electronic Engineering Fudan University Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji University

Vertex cover of complex networks is essentially a major combinatorial optimization problem in network science, which has wide application potentials in engineering. To optimally cover the vertices of complex networks, this paper employs a potential game for the vertex cover problem, designs a novel cost function for network vertices, and proves that the solutions to the minimum value of the potential function are the minimum vertex covering(MVC) states of a general complex network. To achieve the optimal(minimum) covering states, we propose a novel distributed time-variant binary log-linear learning algorithm,and prove that the MVC state of a general complex network is attained under the proposed optimization algorithm. Furthermore, we estimate the upper bound of the convergence rate of the proposed algorithm,and show its effectiveness and superiority using numerical examples with representative complex networks and optimization algorithms.

关键词： minimum vertex cover Nash equilibrium convergence rate scale-free network random graph

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Bearing-Based Adaptive Cooperative Elliptical Circumnavigation control for Multi-Agent Systems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第4期72卷 1787-1799页

作者： Ji, Hongyu Li, Xiang Fudan Univ Dept Elect Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Tongji Univ Inst Complex Networks & Intelligent Syst Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China

This paper investigates the bearing-based cooperative circumnavigation control problems for multi-agent systems to enclose multiple static and moving targets on adaptively designed elliptical orbits. Firstly, a novel method based on estimated relative positions of agents with respect to targets is proposed to adaptively design the elliptical orbits to improve the adaptability of circumnavigation. The relative positions between agents and targets are estimated using only bearing measurements. A bearing-only circumnavigation control law is then proposed to enable a single agent to elliptically circumnavigate multiple targets on the adaptively designed orbit. For a group of agents, the control law for a single agent is extended by incorporating affine formation and collision avoidance. Furthermore, the effectiveness of the proposed orbit design method for enclosing targets and the convergence of the elliptical circumnavigation control laws are analyzed theoretically with their advantages verified by extensive simulations.

关键词： Orbits Extraterrestrial measurements Symmetric matrices Space vehicles Vectors Position measurement Collision avoidance Robot kinematics Multi-agent systems control systems Cooperative elliptical circumnavigation elliptical orbit affine formation collision avoidance

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Distributed Fixed-Time Consensus of Discrete-Time Heterogeneous Multi-Agent Systems via Predictive Mechanism and Lyapunov Approach

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第1期71卷 321-325页

作者： Li, Huiyan Li, Xiang Fudan Univ Elect Engn Dept Adapt Networks & Control Lab Shanghai 200433 Peoples R China Tongji Univ Inst Complex Networks & Intelligent Syst Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China

In this brief, we investigate the fixed-time consensus of discrete-time heterogeneous multi-agent systems with model predictive control. Stability conditions are derived through Lyapunov functions, and the settling time independent of initial conditions is obtained. Based on the sufficient stability conditions, a distributed model predictive controller is designed by optimizing the consensus problem. Numerical examples are conducted to showcase the validity of the derived theoretical results and the effectiveness of the designed controller.

关键词： Fixed-time consensus discrete-time systems heterogeneous multi-agent systems model predictive control

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A Graph Transformer-Driven Approach for Network Robustness Learning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第5期71卷 1992-2005页

作者： Zhang, Yu Li, Jia Ding, Jie Li, Xiang Fudan Univ Sch Informat Sci & Engn Dept Elect Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Tongji Univ Inst Complex Networks & Intelligent Syst Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China

Learning and analysis of network robustness, including controllability robustness and connectivity robustness, is critical for various networked systems against attacks. Traditionally, network robustness is determined by attack simulations, which is very time-consuming and even incapable for large-scale networks. Network Robustness Learning, which is dedicated to learning network robustness with high precision and high speed, provides a powerful tool to analyze network robustness by replacing simulations. In this paper, a novel versatile and unified robustness learning approach based on the customized graph transformer (NRL-GT) is proposed, which consists of a particularly designed backbone and three branches to accomplish the tasks of robustness curve learning, overall robustness learning and synthetic network classification simultaneously. Numerous experiments show that: 1) NRL-GT is a unified learning framework for controllability robustness and connectivity robustness, demonstrating a strong generalization ability to ensure high precision when training and test sets are distributed differently;2) It is theoretically and experimentally demonstrated the proposed graph transformer layer is capable of outperforming classical graph neural networks. Compared to the cutting-edge methods in network robustness learning, NRL-GT can simultaneously perform network robustness learning from multiple aspects and obtains superior results in less time on both synthetic and real-world networks, especially circuit networks and power networks;3) It is worth mentioning that with the transferability of the proposed backbone, NRL-GT is able to deal with complex networks of different sizes for different tasks efficiently and effectively.

关键词： Robustness controllability Transformers complex networks Task analysis Training Graph neural networks controllability connectivity robustness graph transformer robustness learning

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Network controllability Robustness Learning via Spatial Graph Neural networks

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第5期11卷 4045-4058页

作者： Zhang, Yu Ding, Jie Li, Xiang Fudan Univ Sch Informat Sci & Engn Dept Elect Engn Adaptive Networks & Control Lab Shanghai 200433 Peoples R China Tongji Univ Inst Complex Networks & Intelligent Syst Shanghai Res Inst Intelligent Autonomous Syst Shanghai 201210 Peoples R China

controllability of complex networks refers to the ability of a networked system to steer from any initial state to any desired state in a finite time under feasible control inputs. Its robustness reflects the ability of a network to maintain controllability under various attacks, which records a series of controllability values under continuous attacks and can be directly determined by attack simulations. However, such simulation experiments are very time-consuming. It is necessary to design a model to replace simulation experiments to learn network controllability robustness with high precision and high speed, which is the objective of Network controllability Robustness Learning. In this paper, given the superiority of spatial graph neural networks in generating network embedding, a novel controllability robustness learning approach via spatial graph neural networks (CRL-SGNN) is proposed by taking into account both generalization and high precision. In this scheme, controllability robustness related complex network representations that contain network topology information and degree-based node attribute information are first generated through the introduced graph convolutional layer and then sent to the prediction module consisting of multiple branches. A large number of experiments show that: 1) CRL-SGNN can overcome the issue of different distributions between training and test sets with satisfactory performance, implying strong generalization ability of CRL-SGNN;2) Compared to the cutting-edge methods, CRL-SGNN can obtain superior results in less time;and 3) A highlight which cannot be ignored is that the embedding generation module of CRL-SGNN can serve as a transferable feature learning module to deal with complex networks of any size with low prediction error and high efficiency.

关键词： controllability Training Feature extraction Graph neural networks Convolutional neural networks complex networks controllability robustness learning robustness spacial graph neural networks robustness spacial graph neural networks

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Admissibility and controller design for rectangular descriptor time-delay systems☆

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AUTOMATICA 2025年 173卷

作者： Geng, Wen-Tao Lin, Chong Wang, Qing-Guo Qingdao Univ Inst Complex Sci Coll Automat Shandong Key Lab Ind Control Technol Qingdao 26607 Peoples R China Beijing Normal Univ Inst AI & Future Networks Zhuhai 59087 Peoples R China

This paper studies the analysis and stabilizing control problems of rectangular descriptor time-delay systems (RDTDSs), with system matrix pair (E,A) E R-m"n . Firstly, the results for the uniqueness, impulse-freeness, and stability of the solution of rectangular descriptor systems (RDSs) are given in the case of m < n . Secondly, the delay-dependent and delay-independent results based on linear matrix inequalities (LMIs) for RDTDSs are proposed. Subsequently, for RDTDSs with m >= n , the sufficient condition for impulse-freeness is presented. Additionally, admissibility results for RDTDSs with m > n analogous to the case of m < n are provided. Furthermore, a state feedback controller design method is presented, which ensures the admissibility of the closed-loop system when m >= n . Finally, three examples are provided to demonstrate the validity of the obtained results. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Admissible Rectangular descriptor time-delay systems State-equivalent form State feedback control

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Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics

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BIOTECHNOLOGY AND BIOENGINEERING 2024年第1期121卷 366-379页

作者： Espinel-Rios, Sebastian Morabito, Bruno Pohlodek, Johannes Bettenbrock, Katja Klamt, Steffen Findeisen, Rolf Max Planck Inst Dynam Complex Tech Syst Anal & Redesign Biol Networks Magdeburg Germany Yokogawa Insil Biotechnol GmbH Stuttgart Germany Tech Univ Darmstadt Control & Cyber Phys Syst Lab Darmstadt Germany

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity. Metabolic cybergenetic feedback in fed-batch bioreactors exploiting dynamic optimization and model predictive control: Key proteins, like enzyme preg ${p}_{reg}$, adjust using inducible gene systems to achieve various metabolic modes, influenced by fa

关键词： constraint-based modeling dynamic metabolic control metabolic cybergenetics model predictive control optogenetics state estimation

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The Comparative Assessment Methodology of the Demographic Situation in the Regions 16

The Comparative Assessment Methodology of the Demographic Si...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Kozlov, Aleksandr Noga, Nikolai V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Complex Networks Lab Moscow Russia

ISBN: (纸本)9798350337907

Authors proposed methodology that based on the joint use of regression analysis and ranking methods. The using of this methodology allows the comparative analysis of the demographic situation in different regions, the identification of the most effective parameters, and the form of the regional labor resources policy. © 2023 IEEE.

关键词： Regression analysis

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Imperfect Vaccination Evolutionary Game Incorporating Individual Social Difference and Subjective Perception

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2024年第2期11卷 2369-2382页

作者： Li, Cong Dai, Jin-Ying Li, Xiang Fudan Univ Sch Informat Sci & Technol Dept Elect Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Tongji Univ Inst Complex Networks & Intelligent Syst Shanghai Res Inst Intelligent Autonomous Syst Frontiers Sci Ctr Intelligent Autonomous Syst Shanghai 201210 Peoples R China Tongji Univ State Key Lab Intelligent Autonomous Syst Shanghai 201210 Peoples R China

Previous studies have focused on the assumption that individual attitudes toward epidemics, epidemic-related information, and vaccination are identical with much less effort attempting to consider psychological factors and individual heterogeneity. In this article, we employ a two-layer susceptible-infected-recovered-vaccinated/unaware-aware-unaware (SIRV-UAU) coupled network to depict the interplay between epidemic spreading and information diffusion. We propose an imperfect vaccination evolutionary game model integrating prospect theory (PT) to explore the effect of individual subjective perception and social difference on epidemic spreading and vaccination equilibrium. We find that the individual social difference in the epidemic spreading layer has a more significant impact on the epidemic threshold than that in the information diffusion layer. Individual psychological perception and vaccination behavior decision-making determine the vaccination equilibrium: under the PT, vaccination equilibrium increases with the decrease of the rationality coefficient when the vaccination cost is small. Furthermore, we analyze the effect of social differences on individual vaccination behavior decision-making and find that vaccination equilibrium increases with the increase of social reinforcement strength and primary protective probability. Finally, we discuss the effect of infection cost on vaccination equilibrium when the infection cost of vaccinated individuals is less than that of unvaccinated individuals and observe that a small infection cost helps improve the vaccination equilibrium.

关键词： Epidemics Vaccines Behavioral sciences Games Costs Psychology Game theory Epidemic spreading prospect theory (PT) psychological cognition social difference vaccination game

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New Measure for Network controllability Robustness Based on controllable Subspace

New Measure for Network Controllability Robustness Based on ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Liu, Yanwen Ding, Jie Li, Xiang Fudan Univ Sch Informat Sci & Engn Dept Elect Engn Adapt Networks & Control Lab Shanghai 200433 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Inst Complex Networks & Intelligent Syst Shanghai 201210 Peoples R China

ISBN: (纸本)9798350330991;9798350331004

In this paper, we propose a new criteria to measure network controllability robustness based on generic dimension of controllable subspace given a network. Generic dimension curve and controllabillity robustness index are introduced to show network controllability robustness from different aspects. 12 different attack methods are carried out for six network models. It is found that the multiloop structure is useful to maintain network robustness under attacks. Moreover, the networks with single backbone structure are fragile under attacks. Besides, both the increase and protection of driver nodes can enhance the controllability robustness of networks.

关键词： complex networks controllability robustness controllable subspace generic dimension.

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