Pub Date : 2024-12-24DOI: 10.1109/TNSE.2024.3511059
Dusit Tao Niyato
As The incoming Editor-in-Chief of IEEE Transactions on Network Science and Engineering (TNSE), I extend my deepest gratitude to the IEEE Communications Society, the search committee members, and the TNSE community for entrusting me with this significant role. In today's fast-evolving and multidisciplinary publishing environment, the outgoing EiC, Prof. Jianwei Huang, has steered TNSE with remarkable dedication, fostering its growth and maintaining the journal's reputation for excellence. On behalf of the entire TNSE community—including readers, authors, reviewers, editors, and support staff—I sincerely thank Prof. Jianwei Huang for their outstanding contributions and leadership over the past years.
{"title":"Editorial: Introduction of New EiC","authors":"Dusit Tao Niyato","doi":"10.1109/TNSE.2024.3511059","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3511059","url":null,"abstract":"As The incoming Editor-in-Chief of \u0000<sc>IEEE Transactions on Network Science and Engineering</small>\u0000 (TNSE), I extend my deepest gratitude to the IEEE Communications Society, the search committee members, and the TNSE community for entrusting me with this significant role. In today's fast-evolving and multidisciplinary publishing environment, the outgoing EiC, Prof. Jianwei Huang, has steered TNSE with remarkable dedication, fostering its growth and maintaining the journal's reputation for excellence. On behalf of the entire TNSE community—including readers, authors, reviewers, editors, and support staff—I sincerely thank Prof. Jianwei Huang for their outstanding contributions and leadership over the past years.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"3-3"},"PeriodicalIF":6.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10814997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1109/TNSE.2024.3514032
Jianwei Huang
As the Editor-in-Chief of the IEEE Transactions on Network Science and Engineering (TNSE) from 2021 to 2024, it is my distinct pleasure to reflect on the tremendous progress we have made over the past four years. Together, as a vibrant community of researchers, reviewers, and editors, we have consistently endeavored to push the boundaries of network science and engineering. I would like to extend heartfelt gratitude to those who have made this success possible.
{"title":"Editorial: IEEE Transactions on Network Science and Engineering 2025 New Year Editorial","authors":"Jianwei Huang","doi":"10.1109/TNSE.2024.3514032","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3514032","url":null,"abstract":"As the Editor-in-Chief of the \u0000<sc>IEEE Transactions on Network Science and Engineering</small>\u0000 (TNSE) from 2021 to 2024, it is my distinct pleasure to reflect on the tremendous progress we have made over the past four years. Together, as a vibrant community of researchers, reviewers, and editors, we have consistently endeavored to push the boundaries of network science and engineering. I would like to extend heartfelt gratitude to those who have made this success possible.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 1","pages":"1-2"},"PeriodicalIF":6.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10814998","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142890368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In dynamic IoT system, the device may generate multiple heterogeneous computational tasks, that require CPU and GPU co-processing, in each period. Furthermore, different heterogeneous computing tasks have specific requirements for GPU resource types. Realizing real-time scheduling and processing of large-scale hybrid computing tasks with high heterogeneity and dense quantity has become an urgent problem. First, we propose a cloud-based task processing framework that uses multi-level feedback queues to ensure the fairness of large-scale task parallel computing. Second, we decoupled the original problem into a series of mixed-integer nonlinear programming problems using Lyapunov optimization, aiming to reduce the solution complexity of the real-time scheduling problem. Finally, we propose a multi-agent reinforcement learning algorithm, employing long and short-term memory networks with parameter resetting, to generate task offloading decisions in near real-time based on partially knowable future information. Through extensive simulation experiments, we have demonstrated that our algorithm can reduce the average task processing time by approximately 19.95% and enhance the task processing capability of the IoT system by roughly 12.43%, especially in large-scale hybrid computing task systems.
{"title":"Multi-Agent DRL-Based Large-Scale Heterogeneous Task Offloading for Dynamic IoT Systems","authors":"Xiao He;Shanchen Pang;Haiyuan Gui;Kuijie Zhang;Nuanlai Wang;Xue Zhai","doi":"10.1109/TNSE.2024.3521885","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3521885","url":null,"abstract":"In dynamic IoT system, the device may generate multiple heterogeneous computational tasks, that require CPU and GPU co-processing, in each period. Furthermore, different heterogeneous computing tasks have specific requirements for GPU resource types. Realizing real-time scheduling and processing of large-scale hybrid computing tasks with high heterogeneity and dense quantity has become an urgent problem. First, we propose a cloud-based task processing framework that uses multi-level feedback queues to ensure the fairness of large-scale task parallel computing. Second, we decoupled the original problem into a series of mixed-integer nonlinear programming problems using Lyapunov optimization, aiming to reduce the solution complexity of the real-time scheduling problem. Finally, we propose a multi-agent reinforcement learning algorithm, employing long and short-term memory networks with parameter resetting, to generate task offloading decisions in near real-time based on partially knowable future information. Through extensive simulation experiments, we have demonstrated that our algorithm can reduce the average task processing time by approximately 19.95% and enhance the task processing capability of the IoT system by roughly 12.43%, especially in large-scale hybrid computing task systems.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"982-996"},"PeriodicalIF":6.7,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-23DOI: 10.1109/TNSE.2024.3521429
Feng Li;Ya-Nan Wang;Hao Shen
Synchronization of neural networks has found widespread applications in practice. The existing results about the event-triggered synchronization of two-time-scale neural networks/systems mainly design a common event-triggered mechanism using single-rate sampling method on different time scales, which ignores the two-time-scale characteristics and may lead to a suboptimal reduction in communication burden on different time scales. This paper concentrates on dual event-triggered synchronization issues for two-time-scale jumping neural networks. The neural networks are modeled with two-time-scale structures and the changes of jumping parameters follow the Markov process. First, a double-rate sampling method is adopted and the dual event-triggered mechanism is proposed, which contains two separate event-triggered conditions for different time scales states. Then, sufficient conditions are established for the $H_{infty }$ performance analysis of the two-time-scale jumping neural networks while considering the dual event-triggered mechanism. Moreover, based on the above conditions, the controller gains are derived to achieve the event-triggered synchronization of the neural networks. At last, the availability of the proposed approach is demonstrated via two examples, in which image encryption and decryption are used to illustrate the application prospects of the synchronization for two-time-scale jumping neural networks.
{"title":"Dual Event-Triggered Synchronization of Two-Time-Scale Jumping Neural Networks and Its Application in Image Encryption and Decryption","authors":"Feng Li;Ya-Nan Wang;Hao Shen","doi":"10.1109/TNSE.2024.3521429","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3521429","url":null,"abstract":"Synchronization of neural networks has found widespread applications in practice. The existing results about the event-triggered synchronization of two-time-scale neural networks/systems mainly design a <italic>common</i> event-triggered mechanism using <italic>single</i>-rate sampling method on different time scales, which ignores the two-time-scale characteristics and may lead to a suboptimal reduction in communication burden on different time scales. This paper concentrates on <italic>dual</i> event-triggered synchronization issues for two-time-scale jumping neural networks. The neural networks are modeled with two-time-scale structures and the changes of jumping parameters follow the Markov process. First, a <italic>double</i>-rate sampling method is adopted and the dual event-triggered mechanism is proposed, which contains two separate event-triggered conditions for different time scales states. Then, sufficient conditions are established for the <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> performance analysis of the two-time-scale jumping neural networks while considering the dual event-triggered mechanism. Moreover, based on the above conditions, the controller gains are derived to achieve the event-triggered synchronization of the neural networks. At last, the availability of the proposed approach is demonstrated via two examples, in which image encryption and decryption are used to illustrate the application prospects of the synchronization for two-time-scale jumping neural networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"943-954"},"PeriodicalIF":6.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-20DOI: 10.1109/TNSE.2024.3521251
Yu-Qing Zhang;Da-Yan Liu;Driss Boutat;Ze-Hao Wu
In this work, by developing a set of reduced-order local estimators at the networked sensor nodes, we propose a fixed-time and robust algebraic distributed pseudo-state estimation method for fractional-order linear systems. To begin, we introduce a recovered set to construct an invertible transformation at each node, based on an assumption relaxer than the strongly connected assumption. Leveraging this transformation, at each node, the unobservable pseudo-state is recovered, and the relationship between the entire pseudo-state and local observable pseudo-states of the nodes in its recovered set is established. Building upon this framework, we develop the fractional-order modulating functions method to formulate a type of fixed-time pseudo-state estimators using initial-condition-independent algebraic integral formulas to estimate each node's local observable pseudo-state. Subsequently, by collaborating with a group of local observable estimations, the fixed-time distributed estimation for the entire pseudo-state is achieved. The error analysis in noisy cases is also conducted to demonstrate the robustness of our method. Finally, two numerical examples are provided to illustrate the efficacy and scalability of the developed distributed estimation scheme.
{"title":"Fixed-Time and Robust Algebraic Distributed Pseudo-State Estimation for Fractional-Order Systems With Partially Connected Topology","authors":"Yu-Qing Zhang;Da-Yan Liu;Driss Boutat;Ze-Hao Wu","doi":"10.1109/TNSE.2024.3521251","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3521251","url":null,"abstract":"In this work, by developing a set of reduced-order local estimators at the networked sensor nodes, we propose a fixed-time and robust algebraic distributed pseudo-state estimation method for fractional-order linear systems. To begin, we introduce a recovered set to construct an invertible transformation at each node, based on an assumption relaxer than the strongly connected assumption. Leveraging this transformation, at each node, the unobservable pseudo-state is recovered, and the relationship between the entire pseudo-state and local observable pseudo-states of the nodes in its recovered set is established. Building upon this framework, we develop the fractional-order modulating functions method to formulate a type of fixed-time pseudo-state estimators using initial-condition-independent algebraic integral formulas to estimate each node's local observable pseudo-state. Subsequently, by collaborating with a group of local observable estimations, the fixed-time distributed estimation for the entire pseudo-state is achieved. The error analysis in noisy cases is also conducted to demonstrate the robustness of our method. Finally, two numerical examples are provided to illustrate the efficacy and scalability of the developed distributed estimation scheme.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"911-924"},"PeriodicalIF":6.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-20DOI: 10.1109/TNSE.2024.3520625
Meng Yang;Junyong Zhai
This paper investigates the estimate-based adaptive neural secure control method for nonminimum-phase nonlinear networked control systems (NNCSs) under hybrid attacks via dynamic event-triggered scheme. Hybrid attacks such as denial-of-service (DoS) attacks and false data injection (FDI) attacks are assumed to occur in the sensor-to-controller (STC) channel and controller-to-actuator (CTA) channel. When only the system output is obtained, a sampled-data observer is introduced to estimate unknown system states. In order to improve the utilization of communication bandwidth under DoS attacks, a dynamic event-triggered scheme (DETS) is proposed to save network resources. An event-triggered output feedback controller is designed by utilizing neural network (NN) to approximate FDI signals. Based on the proposed dynamic event-triggered control strategy, the selections of the sampling period and the observer gain are derived to ensure the boundedness of the control systems. At last, the efficiency of the control strategy is verified by a numerical example.
{"title":"Estimate-Based Adaptive Neural Secure Control for Nonminimum-Phase Nonlinear Systems With Hybrid Attacks via Dynamic Event-Triggered Scheme","authors":"Meng Yang;Junyong Zhai","doi":"10.1109/TNSE.2024.3520625","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3520625","url":null,"abstract":"This paper investigates the estimate-based adaptive neural secure control method for nonminimum-phase nonlinear networked control systems (NNCSs) under hybrid attacks via dynamic event-triggered scheme. Hybrid attacks such as denial-of-service (DoS) attacks and false data injection (FDI) attacks are assumed to occur in the sensor-to-controller (STC) channel and controller-to-actuator (CTA) channel. When only the system output is obtained, a sampled-data observer is introduced to estimate unknown system states. In order to improve the utilization of communication bandwidth under DoS attacks, a dynamic event-triggered scheme (DETS) is proposed to save network resources. An event-triggered output feedback controller is designed by utilizing neural network (NN) to approximate FDI signals. Based on the proposed dynamic event-triggered control strategy, the selections of the sampling period and the observer gain are derived to ensure the boundedness of the control systems. At last, the efficiency of the control strategy is verified by a numerical example.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"859-869"},"PeriodicalIF":6.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-20DOI: 10.1109/TNSE.2024.3521224
Dan Rong;Haifeng Dai;Yongzheng Sun;Guanghui Wen
Multi-layer network representation has been shown to well characterize real networks, and has revealed some potential phenomenon beyond the interpretation ability of single-layer network successfully. Despite its uniqueness and excitement, intra-layer consensus gets less attention. This paper proposes a discontinuous protocol to address the finite-time intra-layer consensus problem for multi-layer networks. Different from the traditional results of finite-time consensus schemes work only on undirected or detail-balanced networks, a more general case that the network contains a directed spanning tree is considered with a discontinuous protocol. On the other hand, without limiting networks to be of drive-response type or specifically duplexed, the inter-layer topology is assumed to be unidirectional. In addition, both time and energy costs for achieving consensus are studied. Finally, an example is given to verify the theoretical results.
{"title":"Time and Energy Costs for Intra-Layer Consensus of Multi-Layer Networks","authors":"Dan Rong;Haifeng Dai;Yongzheng Sun;Guanghui Wen","doi":"10.1109/TNSE.2024.3521224","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3521224","url":null,"abstract":"Multi-layer network representation has been shown to well characterize real networks, and has revealed some potential phenomenon beyond the interpretation ability of single-layer network successfully. Despite its uniqueness and excitement, intra-layer consensus gets less attention. This paper proposes a discontinuous protocol to address the finite-time intra-layer consensus problem for multi-layer networks. Different from the traditional results of finite-time consensus schemes work only on undirected or detail-balanced networks, a more general case that the network contains a directed spanning tree is considered with a discontinuous protocol. On the other hand, without limiting networks to be of drive-response type or specifically duplexed, the inter-layer topology is assumed to be unidirectional. In addition, both time and energy costs for achieving consensus are studied. Finally, an example is given to verify the theoretical results.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"900-910"},"PeriodicalIF":6.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-19DOI: 10.1109/TNSE.2024.3520130
Javad Zahedi Moghaddam;Aria Nosratinia
This paper studies edge persistence and memory in time-varying (dynamic) networks, and their effect on community detection. In particular, we focus on two models representing network memory, using which we study the asymptotic behavior of community detection in dynamic networks of large size, manifested through their phase transition threshold. In the first part, we adopt a Markovian stochastic block model (SBM) in which the edge probabilities in each network snapshot depend not only on the respective node attributes but also on the previous network snapshot. Under this model, semi-definite relaxation achieves the optimal phase transition bound for exact recovery, as observed in other community detection problems. The adverse effect of edge persistence on perfect recovery is analyzed and highlighted. In the second part, we study networks where underlying communities change slowly compared with network measurements. We model this scenario via a time series of SBM wherein the node attributes are fixed within a window of a certain size and vary independently across windows. The phase transitions are calculated via semidefinite programming which, once again, is asymptotically optimal. Numerical simulations conducted on finite-size networks interpret the asymptotic results.
{"title":"Community Detection in Dynamic Networks: Exact Recovery Under Two Link Evolution Models","authors":"Javad Zahedi Moghaddam;Aria Nosratinia","doi":"10.1109/TNSE.2024.3520130","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3520130","url":null,"abstract":"This paper studies edge persistence and memory in time-varying (dynamic) networks, and their effect on community detection. In particular, we focus on two models representing network memory, using which we study the asymptotic behavior of community detection in dynamic networks of large size, manifested through their phase transition threshold. In the first part, we adopt a Markovian stochastic block model (SBM) in which the edge probabilities in each network snapshot depend not only on the respective node attributes but also on the previous network snapshot. Under this model, semi-definite relaxation achieves the optimal phase transition bound for exact recovery, as observed in other community detection problems. The adverse effect of edge persistence on perfect recovery is analyzed and highlighted. In the second part, we study networks where underlying communities change slowly compared with network measurements. We model this scenario via a time series of SBM wherein the node attributes are fixed within a window of a certain size and vary independently across windows. The phase transitions are calculated via semidefinite programming which, once again, is asymptotically optimal. Numerical simulations conducted on finite-size networks interpret the asymptotic results.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"848-858"},"PeriodicalIF":6.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1109/TNSE.2024.3520100
{"title":"2024 Index IEEE Transactions on Network Science and Engineering Vol. 11","authors":"","doi":"10.1109/TNSE.2024.3520100","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3520100","url":null,"abstract":"","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"11 6","pages":"6718-6822"},"PeriodicalIF":6.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-18DOI: 10.1109/TNSE.2024.3519560
Zuo Wang;Yanzheng Zhu;Xinkai Chen;Chun-Yi Su;Fan Yang
In this paper, the finite-iteration learning consensus formation control issue is studied for a class of multi-agent systems under dual-terminal switching topologies. The completion of consensus formation control requires that each agent can receive control information from the leader indirectly or directly. To relax the restrictions on effective communication relationships among agents, the dual-terminal switching topologies, as a novel communication structure, are proposed for the consensus formation control of multi-agent systems. The dual-terminal switching topologies are composed of switching topologies and iterative-varying topologies corresponding to the variation of communication relations in the time axis and iteration axis, respectively. Therefore, the time axis and iteration axis denote the implication of dual-terminal. The full spanning tree is not required for the proposed topologies at the time terminal, and the missing information among agents can be compensated by the iteration terminal. Based on the communication relationship of dual-terminal switching topologies and the iterative learning control strategy, the corresponding iterative formation error is redefined to perform the desired control task. In order to avoid falling into the infinite iteration of learning, finite-iteration learning control approach is proposed for multi-agent systems with dual-terminal switching topologies. The convergence of formation error is verified by using the contraction-mapping approach. Finally, the effectiveness and availability of the proposed finite-iteration learning-based consensus formation control strategy are measured through a numerical example.
{"title":"Finite-Iterative Learning Consensus Formation Control for Multi-Agent Systems Under Double-Terminal Switching Topologies","authors":"Zuo Wang;Yanzheng Zhu;Xinkai Chen;Chun-Yi Su;Fan Yang","doi":"10.1109/TNSE.2024.3519560","DOIUrl":"https://doi.org/10.1109/TNSE.2024.3519560","url":null,"abstract":"In this paper, the finite-iteration learning consensus formation control issue is studied for a class of multi-agent systems under dual-terminal switching topologies. The completion of consensus formation control requires that each agent can receive control information from the leader indirectly or directly. To relax the restrictions on effective communication relationships among agents, the dual-terminal switching topologies, as a novel communication structure, are proposed for the consensus formation control of multi-agent systems. The dual-terminal switching topologies are composed of switching topologies and iterative-varying topologies corresponding to the variation of communication relations in the time axis and iteration axis, respectively. Therefore, the time axis and iteration axis denote the implication of dual-terminal. The full spanning tree is not required for the proposed topologies at the time terminal, and the missing information among agents can be compensated by the iteration terminal. Based on the communication relationship of dual-terminal switching topologies and the iterative learning control strategy, the corresponding iterative formation error is redefined to perform the desired control task. In order to avoid falling into the infinite iteration of learning, finite-iteration learning control approach is proposed for multi-agent systems with dual-terminal switching topologies. The convergence of formation error is verified by using the contraction-mapping approach. Finally, the effectiveness and availability of the proposed finite-iteration learning-based consensus formation control strategy are measured through a numerical example.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 2","pages":"803-813"},"PeriodicalIF":6.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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