Pub Date : 2025-12-25DOI: 10.1109/TCNS.2025.3641584
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/TCNS.2025.3641584","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3641584","url":null,"abstract":"","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 4","pages":"3109-3110"},"PeriodicalIF":5.0,"publicationDate":"2025-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11315890","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-12-01Epub Date: 2025-07-18DOI: 10.1109/tcns.2025.3590383
Mahtab Talaei, Apostolos I Rikos, Alex Olshevsky, Laura F White, Ioannis Ch Paschalidis
Motivated by the swift global transmission of infectious diseases, we present a comprehensive framework for network-based epidemic control. Our aim is to curb epidemics using two different approaches. In the first approach, we introduce an optimization strategy that optimally reduces travel rates. We analyze the convergence of this strategy and show that it hinges on the network structure to minimize infection spread. In the second approach, we expand the classic SIR model by incorporating and optimizing quarantined states to strategically contain the epidemic. We show that this problem reduces to the problem of matrix balancing. We establish a link between optimization constraints and the epidemic's reproduction number, highlighting the relationship between network structure and disease dynamics. We demonstrate that applying augmented primal-dual gradient dynamics to the optimal quarantine problem ensures exponential convergence to a stationary point. We conclude by validating our approaches using simulation studies that leverage public data from counties in the state of Massachusetts.
{"title":"Network-Based Epidemic Control Through Optimal Travel and Quarantine Management.","authors":"Mahtab Talaei, Apostolos I Rikos, Alex Olshevsky, Laura F White, Ioannis Ch Paschalidis","doi":"10.1109/tcns.2025.3590383","DOIUrl":"10.1109/tcns.2025.3590383","url":null,"abstract":"<p><p>Motivated by the swift global transmission of infectious diseases, we present a comprehensive framework for network-based epidemic control. Our aim is to curb epidemics using two different approaches. In the first approach, we introduce an optimization strategy that optimally reduces travel rates. We analyze the convergence of this strategy and show that it hinges on the network structure to minimize infection spread. In the second approach, we expand the classic SIR model by incorporating and optimizing quarantined states to strategically contain the epidemic. We show that this problem reduces to the problem of matrix balancing. We establish a link between optimization constraints and the epidemic's reproduction number, highlighting the relationship between network structure and disease dynamics. We demonstrate that applying augmented primal-dual gradient dynamics to the optimal quarantine problem ensures exponential convergence to a stationary point. We conclude by validating our approaches using simulation studies that leverage public data from counties in the state of Massachusetts.</p>","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 4","pages":"2726-2738"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12830048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146055078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-10-17DOI: 10.1109/TCNS.2025.3623001
Qin Wang;Hanyu Yin;Guangyu Zhu;Yang Yi;Jun Yang
Accurate distance-based formation control is frequently compromised by the presence of multiple equilibria. A standard gradient law can direct a multiagent system to the zero-gradient set; however, it may fail to attain the unique desired configuration, thereby jeopardizing the overall mission reliability. To overcome this limitation while maintaining collision safety, we put forward a fully distributed, globally stabilizing control framework. First, a scalable graph-decomposition algorithm is employed to verify whether a formation graph exhibits the requisite cascade structure and automatically extract its interconnections. Subsequently, based on the cascade structure derived from the algorithm, a distributed perturbed gradient control law is implemented to facilitate the multiagent system in achieving the desired globally stable formation. Furthermore, the distributed adaptive velocity estimation law is introduced, relying solely on the relative positions of the agents, thus eliminating the necessity to ascertain the velocities of neighboring agents. This method effectively addresses the challenge of simultaneously ensuring collision avoidance and maintaining the desired formation shape. Finally, the global convergence and stability properties are obtained using the cascade system stability theory and adaptive control theory. Simulations are included to validate the effectiveness of the globally asymptotically stable formation control strategy.
{"title":"Distributed Adaptive Global Stabilization of a Class of Rigid Formation Systems","authors":"Qin Wang;Hanyu Yin;Guangyu Zhu;Yang Yi;Jun Yang","doi":"10.1109/TCNS.2025.3623001","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3623001","url":null,"abstract":"Accurate distance-based formation control is frequently compromised by the presence of multiple equilibria. A standard gradient law can direct a multiagent system to the zero-gradient set; however, it may fail to attain the unique desired configuration, thereby jeopardizing the overall mission reliability. To overcome this limitation while maintaining collision safety, we put forward a fully distributed, globally stabilizing control framework. First, a scalable graph-decomposition algorithm is employed to verify whether a formation graph exhibits the requisite cascade structure and automatically extract its interconnections. Subsequently, based on the cascade structure derived from the algorithm, a distributed perturbed gradient control law is implemented to facilitate the multiagent system in achieving the desired globally stable formation. Furthermore, the distributed adaptive velocity estimation law is introduced, relying solely on the relative positions of the agents, thus eliminating the necessity to ascertain the velocities of neighboring agents. This method effectively addresses the challenge of simultaneously ensuring collision avoidance and maintaining the desired formation shape. Finally, the global convergence and stability properties are obtained using the cascade system stability theory and adaptive control theory. Simulations are included to validate the effectiveness of the globally asymptotically stable formation control strategy.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 4","pages":"3096-3108"},"PeriodicalIF":5.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-09-23DOI: 10.1109/TCNS.2025.3613571
Cui-Qin Ma;Jiashuo Liu;Yu Kang;Yun-Bo Zhao
The node-to-node bipartite consensus problem of multiagent systems with a two-layer network consisting of general linear dynamics is investigated. A novel observer technique composed of two state and fault observers is introduced in the presence of deception attack and sensor faults. Then, an observer-based fault-tolerant control framework is proposed to deal with joint effects of sensor faults and deception attack obeying the Bernoulli distribution launched from malicious adversaries. By exploiting matrix analysis and Lyapunov stability theory, sufficient conditions for achieving node-to-node mean-square bounded bipartite consensus are obtained. Numerical examples illustrate the effectiveness of the proposed approach.
{"title":"Node-to-Node Fault-Tolerant Control of Layered Multiagent Systems Under Deception Attack","authors":"Cui-Qin Ma;Jiashuo Liu;Yu Kang;Yun-Bo Zhao","doi":"10.1109/TCNS.2025.3613571","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3613571","url":null,"abstract":"The node-to-node bipartite consensus problem of multiagent systems with a two-layer network consisting of general linear dynamics is investigated. A novel observer technique composed of two state and fault observers is introduced in the presence of deception attack and sensor faults. Then, an observer-based fault-tolerant control framework is proposed to deal with joint effects of sensor faults and deception attack obeying the Bernoulli distribution launched from malicious adversaries. By exploiting matrix analysis and Lyapunov stability theory, sufficient conditions for achieving node-to-node mean-square bounded bipartite consensus are obtained. Numerical examples illustrate the effectiveness of the proposed approach.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 4","pages":"3086-3095"},"PeriodicalIF":5.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-09-22DOI: 10.1109/TCNS.2025.3606301
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/TCNS.2025.3606301","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3606301","url":null,"abstract":"","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 3","pages":"2460-2461"},"PeriodicalIF":5.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11175266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145110275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-09-22DOI: 10.1109/TCNS.2025.3606271
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/TCNS.2025.3606271","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3606271","url":null,"abstract":"","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 3","pages":"C2-C2"},"PeriodicalIF":5.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11175264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145315440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-09-12DOI: 10.1109/TCNS.2025.3609433
Rui Guo;Jianwen Feng;Jingyi Wang;Guanrong Chen;Tingwen Huang;Xinzhi Liu
This article investigates the formation stabilization problem of continuous-time nonlinear multiagent systems subject to state constraints, input constraints, and external disturbances. To solve this issue, a dynamic event-triggered distributed model-predictive control algorithm is developed, integrating a control configuration that simultaneously considers both the triggering scheme and the variable prediction horizon. Specifically, a dynamic event-triggered mechanism based on feasibility analysis is proposed to adaptively adjust the triggering threshold, thereby reducing computational and communication burdens while preventing Zeno behavior. Meanwhile, a variable prediction horizon scheme is designed for each agent to effectively shorten the prediction horizon of the involved optimal control problem, which reduces the computational complexity of the proposed algorithm. Furthermore, theoretical conditions are established to ensure the recursive feasibility and closed-loop stability of the algorithm. Finally, theoretical results are verified through a numerical example with comparison analysis.
{"title":"Dynamic Event-Triggered DMPC With Variable Prediction Horizon for Disturbed Nonlinear Multiagent Systems","authors":"Rui Guo;Jianwen Feng;Jingyi Wang;Guanrong Chen;Tingwen Huang;Xinzhi Liu","doi":"10.1109/TCNS.2025.3609433","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3609433","url":null,"abstract":"This article investigates the formation stabilization problem of continuous-time nonlinear multiagent systems subject to state constraints, input constraints, and external disturbances. To solve this issue, a dynamic event-triggered distributed model-predictive control algorithm is developed, integrating a control configuration that simultaneously considers both the triggering scheme and the variable prediction horizon. Specifically, a dynamic event-triggered mechanism based on feasibility analysis is proposed to adaptively adjust the triggering threshold, thereby reducing computational and communication burdens while preventing Zeno behavior. Meanwhile, a variable prediction horizon scheme is designed for each agent to effectively shorten the prediction horizon of the involved optimal control problem, which reduces the computational complexity of the proposed algorithm. Furthermore, theoretical conditions are established to ensure the recursive feasibility and closed-loop stability of the algorithm. Finally, theoretical results are verified through a numerical example with comparison analysis.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 4","pages":"3073-3085"},"PeriodicalIF":5.0,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-09-09DOI: 10.1109/TCNS.2025.3608058
Renzhi Zhang;Jie Lian;Feiyue Wu
This article investigates the sampled-data distributed control of parabolic partial differential equation (PDE) systems with noncollocated observation. Both the measurement outputs and control inputs are sampled and transmitted through a delayed network, leading to the issue of spatiotemporally asynchronous sampled-data. A predictor-based observer is proposed, which achieves noncollocated dynamic feedback control while addressing the network-induced input delay. Furthermore, the try-once-discard protocol is extended to networked PDE systems for the first time, which results in an impulse sampled-data PDE system. A generalized Halanay inequality is newly proposed to address the spatiotemporally asynchronous sampling, complemented by the Lyapunov窶適rasovskii method to establish exponential convergence conditions for closed-loop PDEs. With the help of the $C_{0}$ semigroup theory, the well-posedness analysis of the impulsive sampled-data closed-loop PDEs is given by constructing an inhomogeneous abstract Cauchy problem. Finally, a simulation example verifies the effectiveness of the proposed method.
{"title":"Network-Based Distributed Control of Parabolic PDE Systems Under Noncollocated Observation","authors":"Renzhi Zhang;Jie Lian;Feiyue Wu","doi":"10.1109/TCNS.2025.3608058","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3608058","url":null,"abstract":"This article investigates the sampled-data distributed control of parabolic partial differential equation (PDE) systems with noncollocated observation. Both the measurement outputs and control inputs are sampled and transmitted through a delayed network, leading to the issue of spatiotemporally asynchronous sampled-data. A predictor-based observer is proposed, which achieves noncollocated dynamic feedback control while addressing the network-induced input delay. Furthermore, the try-once-discard protocol is extended to networked PDE systems for the first time, which results in an impulse sampled-data PDE system. A generalized Halanay inequality is newly proposed to address the spatiotemporally asynchronous sampling, complemented by the Lyapunov窶適rasovskii method to establish exponential convergence conditions for closed-loop PDEs. With the help of the <inline-formula><tex-math>$C_{0}$</tex-math></inline-formula> semigroup theory, the well-posedness analysis of the impulsive sampled-data closed-loop PDEs is given by constructing an inhomogeneous abstract Cauchy problem. Finally, a simulation example verifies the effectiveness of the proposed method.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 4","pages":"3049-3061"},"PeriodicalIF":5.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-09-09DOI: 10.1109/TCNS.2025.3608004
Kunpeng Zhang;Lei Xu;Xinlei Yi;Guanghui Wen;Lihua Xie;Tianyou Chai;Tao Yang
This article considers the distributed bandit convex optimization problem with time-varying constraints. In this problem, the global loss function is the average of all the local convex loss functions, which are unknown beforehand. Each agent iteratively makes its own decision subject to time-varying inequality constraints, which can be violated but are fulfilled in the long run. For a uniformly jointly strongly connected time-varying directed graph, a distributed bandit online primal–dual projection algorithm with one-point sampling is proposed. We show that sublinear dynamic network regret and network cumulative constraint violation (CCV) are achieved if the path length of the benchmark also increases in a sublinear manner. In addition, an $mathcal {O}({T^{3/4 + g}})$ static network regret bound and an $mathcal {O} ({{T^{1 - {g}/2}}})$ network CCV bound are established, where $T$ is the total number of iterations and $g in ({0,1/4})$ is a tradeoff parameter. Moreover, a reduced static network regret bound $mathcal {O} ({{T^{2/3 + 4g /3}}})$ is established for strongly convex local loss functions. Finally, a numerical example is presented to validate the theoretical results.
{"title":"One-Point Sampling for Distributed Bandit Convex Optimization With Time-Varying Constraints","authors":"Kunpeng Zhang;Lei Xu;Xinlei Yi;Guanghui Wen;Lihua Xie;Tianyou Chai;Tao Yang","doi":"10.1109/TCNS.2025.3608004","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3608004","url":null,"abstract":"This article considers the distributed bandit convex optimization problem with time-varying constraints. In this problem, the global loss function is the average of all the local convex loss functions, which are unknown beforehand. Each agent iteratively makes its own decision subject to time-varying inequality constraints, which can be violated but are fulfilled in the long run. For a uniformly jointly strongly connected time-varying directed graph, a distributed bandit online primal–dual projection algorithm with one-point sampling is proposed. We show that sublinear dynamic network regret and network cumulative constraint violation (CCV) are achieved if the path length of the benchmark also increases in a sublinear manner. In addition, an <inline-formula><tex-math>$mathcal {O}({T^{3/4 + g}})$</tex-math></inline-formula> static network regret bound and an <inline-formula><tex-math>$mathcal {O} ({{T^{1 - {g}/2}}})$</tex-math></inline-formula> network CCV bound are established, where <inline-formula><tex-math>$T$</tex-math></inline-formula> is the total number of iterations and <inline-formula><tex-math>$g in ({0,1/4})$</tex-math></inline-formula> is a tradeoff parameter. Moreover, a reduced static network regret bound <inline-formula><tex-math>$mathcal {O} ({{T^{2/3 + 4g /3}}})$</tex-math></inline-formula> is established for strongly convex local loss functions. Finally, a numerical example is presented to validate the theoretical results.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 4","pages":"3062-3072"},"PeriodicalIF":5.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-06-20DOI: 10.1109/TCNS.2025.3573219
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/TCNS.2025.3573219","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3573219","url":null,"abstract":"","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1828-1829"},"PeriodicalIF":4.0,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045660","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}
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