Pub Date : 2025-01-07DOI: 10.1109/TCNS.2025.3526718
Mark Christianen;Sanne van Kempen;Maria Vlasiou;Bert Zwart
The optimal power flow (OPF) problem is one of the most fundamental problems in power system operations. The nonlinear ac power flow equations that model different physical laws (together with operational constraints) lay the foundation for the feasibility region of the OPF problem. While significant research has focused on convex relaxations, which are approaches to solve an OPF problem by enlarging the true feasibility region, the opposite approach of convex restrictions offers valuable insights as well. Convex restrictions, including polyhedral restrictions, reduce the true feasible region to a convex region, ensuring that it contains only feasible points. In this work, we develop a sequential optimization method that offers a scalable way to obtain (bounds on) solutions to OPF problems for distribution networks. To do so, we first develop sufficient conditions for the existence of feasible power flow solutions in the neighborhood of a specific (feasible) operating point in distribution networks; second, based on these conditions, we construct a polyhedral restriction of the feasibility region. Our numerical results demonstrate the efficacy of the sequential optimization method as an alternative to existing approaches to obtain (bounds on) solutions to OPF problems for distribution networks. By construction, the optimization problems within the defined restrictions can be solved in polynomial time and are guaranteed to have feasible solutions.
{"title":"Polyhedral Restrictions of Feasibility Regions in Optimal Power Flow for Distribution Networks","authors":"Mark Christianen;Sanne van Kempen;Maria Vlasiou;Bert Zwart","doi":"10.1109/TCNS.2025.3526718","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526718","url":null,"abstract":"The optimal power flow (OPF) problem is one of the most fundamental problems in power system operations. The nonlinear ac power flow equations that model different physical laws (together with operational constraints) lay the foundation for the feasibility region of the OPF problem. While significant research has focused on convex relaxations, which are approaches to solve an OPF problem by enlarging the true feasibility region, the opposite approach of convex restrictions offers valuable insights as well. Convex restrictions, including polyhedral restrictions, reduce the true feasible region to a convex region, ensuring that it contains only feasible points. In this work, we develop a sequential optimization method that offers a scalable way to obtain (bounds on) solutions to OPF problems for distribution networks. To do so, we first develop sufficient conditions for the existence of feasible power flow solutions in the neighborhood of a specific (feasible) operating point in distribution networks; second, based on these conditions, we construct a polyhedral restriction of the feasibility region. Our numerical results demonstrate the efficacy of the sequential optimization method as an alternative to existing approaches to obtain (bounds on) solutions to OPF problems for distribution networks. By construction, the optimization problems within the defined restrictions can be solved in polynomial time and are guaranteed to have feasible solutions.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1587-1599"},"PeriodicalIF":4.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331609","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 : 2025-01-07DOI: 10.1109/TCNS.2025.3526713
Peng Wang;Haibin Shao;Lulu Pan;Weiwu Yan;Ning Li
Multiagent consensus algorithms have emerged as foundational tools across a spectrum of applications, and matrix-weighted consensus ones are capable of characterizing cross-dimensional interdependence. Yet, their potential is often shadowed by a pressing concern: the privacy of agents' initial values, which frequently represent sensitive data or proprietary information. A computationally light privacy-preserving mechanism for matrix-weighted average consensus (MAC) algorithms is proposed in response to the concern of agents' privacy. In the mechanism, agents' states are first perturbed and then multiplied by the matrix weights before being sent to their neighbors. Both the perturbation and the matrix weight are neighbor-dependent, i.e., they may be selected to be different for different neighbors, and they can be selected independently to mask the true state of an agent. The proposed mechanism can simultaneously guarantee the privacy of initial values and accurate average consensus. The additional computational burden that an agent bears is only the addition of vectors in the same dimension as its state compared to the original MAC algorithm. Through practical case studies with a peer-to-peer transactive energy system, we demonstrate the tangible implications of safeguarding initial value privacy with the proposed mechanism.
{"title":"Computationally Light Privacy Preservation of Matrix-Weighted Average Consensus","authors":"Peng Wang;Haibin Shao;Lulu Pan;Weiwu Yan;Ning Li","doi":"10.1109/TCNS.2025.3526713","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526713","url":null,"abstract":"Multiagent consensus algorithms have emerged as foundational tools across a spectrum of applications, and matrix-weighted consensus ones are capable of characterizing cross-dimensional interdependence. Yet, their potential is often shadowed by a pressing concern: the privacy of agents' initial values, which frequently represent sensitive data or proprietary information. A computationally light privacy-preserving mechanism for matrix-weighted average consensus (MAC) algorithms is proposed in response to the concern of agents' privacy. In the mechanism, agents' states are first perturbed and then multiplied by the matrix weights before being sent to their neighbors. Both the perturbation and the matrix weight are neighbor-dependent, i.e., they may be selected to be different for different neighbors, and they can be selected independently to mask the true state of an agent. The proposed mechanism can simultaneously guarantee the privacy of initial values and accurate average consensus. The additional computational burden that an agent bears is only the addition of vectors in the same dimension as its state compared to the original MAC algorithm. Through practical case studies with a peer-to-peer transactive energy system, we demonstrate the tangible implications of safeguarding initial value privacy with the proposed mechanism.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1651-1661"},"PeriodicalIF":4.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331543","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}
In this article, a distributed algorithm is proposed to identify the node and edge numbers of anonymous leader–follower networks. The present distributed method works merely by exchanging scalar information by local intercommunication. The merit of the algorithm lies in identifying the required network parameters in time-varying topological networks without special initialization. Sufficient conditions are derived for cascading systems to theoretically guarantee the exponential convergence of the individual estimation to the total number of nodes or edges merely by local information exchange. Finally, numerical simulations are conducted to substantiate the effectiveness of the present identification algorithm.
{"title":"Distributed Identification for Node and Edge Numbers of Time-Varying Anonymous Networks","authors":"Xingjian Liu;Hai-Tao Zhang;Haosen Cao;Ning Xing;Bowen Xu;Jiayu Zou;Haofei Meng","doi":"10.1109/TCNS.2025.3526549","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526549","url":null,"abstract":"In this article, a distributed algorithm is proposed to identify the node and edge numbers of anonymous leader–follower networks. The present distributed method works merely by exchanging scalar information by local intercommunication. The merit of the algorithm lies in identifying the required network parameters in time-varying topological networks without special initialization. Sufficient conditions are derived for cascading systems to theoretically guarantee the exponential convergence of the individual estimation to the total number of nodes or edges merely by local information exchange. Finally, numerical simulations are conducted to substantiate the effectiveness of the present identification algorithm.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1531-1539"},"PeriodicalIF":4.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331517","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 : 2025-01-07DOI: 10.1109/TCNS.2025.3526729
Yi Zheng;Yueyan Zhang;Shaoyuan Li;Min Luo
This article presents a codesign control scheme that integrates distributed model predictive control with dynamic quantizers for networked nonlinear continuous systems operating under limited communication bandwidth. Each subsystem-based model predictive control formulation considers the dynamic quantizer's influence within the optimization problem in the proposed method. Both the quantizer parameters and the control law are optimized simultaneously in real time, which offers potential improvements in the performance of the closed-loop system within a certain communication limitation. A stability constraint based on small-gain Lyapunov theory is designed for each subsystem's controller, allowing for a relaxation of the restrictions on the closed-loop subsystems compared to decentralized approaches. In addition, a sufficient condition that ensures the stability of the overall system is provided. Simulation results demonstrate the effectiveness of the proposed codesign method.
{"title":"Codesign of Dynamic Quantizer and Small-Gain-Based Distributed Model Predictive Control of Nonlinear Continuous System","authors":"Yi Zheng;Yueyan Zhang;Shaoyuan Li;Min Luo","doi":"10.1109/TCNS.2025.3526729","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526729","url":null,"abstract":"This article presents a codesign control scheme that integrates distributed model predictive control with dynamic quantizers for networked nonlinear continuous systems operating under limited communication bandwidth. Each subsystem-based model predictive control formulation considers the dynamic quantizer's influence within the optimization problem in the proposed method. Both the quantizer parameters and the control law are optimized simultaneously in real time, which offers potential improvements in the performance of the closed-loop system within a certain communication limitation. A stability constraint based on small-gain Lyapunov theory is designed for each subsystem's controller, allowing for a relaxation of the restrictions on the closed-loop subsystems compared to decentralized approaches. In addition, a sufficient condition that ensures the stability of the overall system is provided. Simulation results demonstrate the effectiveness of the proposed codesign method.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1699-1708"},"PeriodicalIF":4.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331538","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}
We introduce an innovative approach for analyzing strategic interactions in transportation networks featuring mobility-on-demand (MoD) services. This study focuses on achieving company–traveler equilibria, whereby a single company optimizes pricing and routing decisions to maximize profitability while considering travelers' mode choices, modeled via a multinomial logit model (MNL). Although profit maximization problems have been extensively studied in the field of revenue management across various domains, their application to transportation networks poses unique challenges, such as the influence of network topology and additional constraints (e.g., flow conservation, rebalancing, etc.). To address the inherent nonlinear relationship arising from endogenous travel demand, we shift our domain space from price to market share. Subsequently, we derive prices using a direct one-to-one correspondence within the MNL. This work is the first effort in leveraging such novel techniques in the context of transportation network analysis. Remarkably, the proposed reformulation results in an equivalent problem exhibiting convexity, offering computational efficiency and interpretability. By solving the Karush–Kuhn–Tucker conditions, we characterize user equilibrium with the generalized route cost, which incorporates the operating cost by rebalancing and travelers' disutility caused by congestion. Our approach is empirically validated through a numerical analysis conducted on the widely recognized Sioux Falls network. The results underscore the effectiveness and practical applicability of our method in analyzing transportation networks featuring MoD services and open the stage for important future investigations.
{"title":"Strategic Pricing and Routing to Maximize Profit in Congested Roads Considering Interactions With Travelers","authors":"Youngseo Kim;Ning Duan;Gioele Zardini;Samitha Samaranayake;Damon Wischik","doi":"10.1109/TCNS.2025.3526714","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526714","url":null,"abstract":"We introduce an innovative approach for analyzing strategic interactions in transportation networks featuring mobility-on-demand (MoD) services. This study focuses on achieving company–traveler equilibria, whereby a single company optimizes pricing and routing decisions to maximize profitability while considering travelers' mode choices, modeled via a multinomial logit model (MNL). Although profit maximization problems have been extensively studied in the field of revenue management across various domains, their application to transportation networks poses unique challenges, such as the influence of network topology and additional constraints (e.g., flow conservation, rebalancing, etc.). To address the inherent nonlinear relationship arising from endogenous travel demand, we shift our domain space from price to market share. Subsequently, we derive prices using a direct one-to-one correspondence within the MNL. This work is the first effort in leveraging such novel techniques in the context of transportation network analysis. Remarkably, the proposed reformulation results in an equivalent problem exhibiting convexity, offering computational efficiency and interpretability. By solving the Karush–Kuhn–Tucker conditions, we characterize user equilibrium with the generalized route cost, which incorporates the operating cost by rebalancing and travelers' disutility caused by congestion. Our approach is empirically validated through a numerical analysis conducted on the widely recognized Sioux Falls network. The results underscore the effectiveness and practical applicability of our method in analyzing transportation networks featuring MoD services and open the stage for important future investigations.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1638-1650"},"PeriodicalIF":4.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331535","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 : 2025-01-07DOI: 10.1109/TCNS.2025.3526717
Federico M. Zegers;Sean Phillips;Gregory P. Hicks
This work investigates the coverage control problem over a static, compact, and convex workspace and develops a hybrid extension of the continuous-time Lloyd algorithm. Each agent in a multiagent system (MAS) is equipped with a timer mechanism that generates intermittent measurement and control update events, which may occur asynchronously between agents. Between consecutive event times, as determined by the corresponding timer mechanism, the controller of each agent is held constant. These controllers are shown to drive the configuration of the MAS into a neighborhood of the set of centroidal Voronoi configurations, i.e., the minimizers of the standard locational cost. The combination of continuous-time dynamics with intermittently updated control inputs is modeled as a hybrid system. The coverage objective is posed as a set attractivity problem for hybrid systems, where an invariance-based convergence analysis yields sufficient conditions that ensure maximal solutions of the hybrid system asymptotically converge to a desired set. A brief simulation example is included to showcase the result.
{"title":"Timer-Based Coverage Control for Mobile Sensors","authors":"Federico M. Zegers;Sean Phillips;Gregory P. Hicks","doi":"10.1109/TCNS.2025.3526717","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526717","url":null,"abstract":"This work investigates the coverage control problem over a static, compact, and convex workspace and develops a hybrid extension of the continuous-time Lloyd algorithm. Each agent in a multiagent system (MAS) is equipped with a timer mechanism that generates intermittent measurement and control update events, which may occur asynchronously between agents. Between consecutive event times, as determined by the corresponding timer mechanism, the controller of each agent is held constant. These controllers are shown to drive the configuration of the MAS into a neighborhood of the set of centroidal Voronoi configurations, i.e., the minimizers of the standard locational cost. The combination of continuous-time dynamics with intermittently updated control inputs is modeled as a hybrid system. The coverage objective is posed as a set attractivity problem for hybrid systems, where an invariance-based convergence analysis yields sufficient conditions that ensure maximal solutions of the hybrid system asymptotically converge to a desired set. A brief simulation example is included to showcase the result.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1600-1612"},"PeriodicalIF":4.0,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331573","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 : 2025-01-06DOI: 10.1109/TCNS.2025.3526566
Jiayu Zou;Hai-Tao Zhang;Chao Zhai;Ning Xing;Yong Ma;Xingjian Liu
It has long posed a challenging task to optimally deploy multiagent systems (MASs) to cooperatively coverage poriferous environments in real cooperative detection applications. In response to this challenge, this article proposes a hierarchical coverage control (HCC) protocol for MASs to perform sector-based coverage operations. First, a distributed Voronoi partition-based sweep-and-assign protocol, combined with a sectorial partition method, is developed, enabling the segmentation of a whole poriferous region into multiple sectorial sub-subregions. Following this procedure, an extreme search scheme is developed to determine the optimal amount of agents for each subregion. After this allocation, a distributed controller is proposed to deploy the agents, considering nonholonomic constraints, into designated niche positions. In addition, sufficient conditions are derived to guarantee the asymptotical stability of the present HCC. The analytical challenge of the present study stems from the nonconvex characteristics inherent in the loss function induced by porous environments. This nonconvexity renders the task of globally optimizing the assignment of MASs intractable. Finally, numerical simulations are conducted to validate the effectiveness of the present coverage control approach.
{"title":"Cooperative Hierarchical Coverage Control of Multiagent Systems With Nonholonomic Constraints in Poriferous Environments","authors":"Jiayu Zou;Hai-Tao Zhang;Chao Zhai;Ning Xing;Yong Ma;Xingjian Liu","doi":"10.1109/TCNS.2025.3526566","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526566","url":null,"abstract":"It has long posed a challenging task to optimally deploy multiagent systems (MASs) to cooperatively coverage poriferous environments in real cooperative detection applications. In response to this challenge, this article proposes a hierarchical coverage control (HCC) protocol for MASs to perform sector-based coverage operations. First, a distributed Voronoi partition-based sweep-and-assign protocol, combined with a sectorial partition method, is developed, enabling the segmentation of a whole poriferous region into multiple sectorial sub-subregions. Following this procedure, an extreme search scheme is developed to determine the optimal amount of agents for each subregion. After this allocation, a distributed controller is proposed to deploy the agents, considering nonholonomic constraints, into designated niche positions. In addition, sufficient conditions are derived to guarantee the asymptotical stability of the present HCC. The analytical challenge of the present study stems from the nonconvex characteristics inherent in the loss function induced by porous environments. This nonconvexity renders the task of globally optimizing the assignment of MASs intractable. Finally, numerical simulations are conducted to validate the effectiveness of the present coverage control approach.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1512-1520"},"PeriodicalIF":4.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331578","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 : 2025-01-06DOI: 10.1109/TCNS.2025.3526324
Ge Guo;Zeng-Di Zhou;Renyongkang Zhang
This article investigates a distributed time-varying optimization problem with inequality constraints, aiming to find finite-time and fixed-time convergent solutions free from initialization. A nonsmooth optimization algorithm for state consensus achieving within a finite or fixed time is presented by designing a projection-based log-barrier penalty cost function to meet the constraints and introducing integral sliding mode subsystems to guarantee zero-gradient-sum. With the use of the projection idea, the penalized functions are always well defined (i.e., satisfying the logarithmic definition) for any system states, which avoids initializing of certain parameters. An adaptive gain scheme without any extra global information is presented. The time-varying zero-gradient-sum method here is feasible for cost functions with nonidentical Hessian matrixes, and applicable to finite-time or fixed-time optimal consensus tracking. The effectiveness and superiority of our algorithms are verified with numerical simulations.
{"title":"Distributed Time-Varying Constrained Convex Optimization: Finite-Time/Fixed-Time Convergence","authors":"Ge Guo;Zeng-Di Zhou;Renyongkang Zhang","doi":"10.1109/TCNS.2025.3526324","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526324","url":null,"abstract":"This article investigates a distributed time-varying optimization problem with inequality constraints, aiming to find finite-time and fixed-time convergent solutions free from initialization. A nonsmooth optimization algorithm for state consensus achieving within a finite or fixed time is presented by designing a projection-based log-barrier penalty cost function to meet the constraints and introducing integral sliding mode subsystems to guarantee zero-gradient-sum. With the use of the projection idea, the penalized functions are always well defined (i.e., satisfying the logarithmic definition) for any system states, which avoids initializing of certain parameters. An adaptive gain scheme without any extra global information is presented. The time-varying zero-gradient-sum method here is feasible for cost functions with nonidentical Hessian matrixes, and applicable to finite-time or fixed-time optimal consensus tracking. The effectiveness and superiority of our algorithms are verified with numerical simulations.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1500-1511"},"PeriodicalIF":4.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331579","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 : 2025-01-06DOI: 10.1109/TCNS.2025.3526569
Emre Yildirim;Tansel Yucelen
In this article, we study multiagent networks over directed graph topologies involving nodes subject to exogenous disturbances (i.e., misbehaving nodes) and nodes that receive feedback control signals (i.e., driver nodes) for the purpose of suppressing the adverse effects of misbehaving nodes. The number of driver nodes can be less than the total number of nodes in the multiagent network. Specifically, we propose proportional–integral feedback controllers to be executed by driver nodes. These controllers guarantee the stability of the overall multiagent network in the sense of input-to-state stability (i.e., they make the resulting closed-loop system matrix Hurwitz). Furthermore, we utilize a graph-theoretical approach that allows users to find the steady-state values of critical nodes without requiring the knowledge of the Laplacian matrix of the overall multiagent network. The results presented in this article pave the way for understanding how driver nodes need to be selected to suppress the effect of misbehaving nodes on the neighborhood of critical nodes, which is further illustrated through illustrative numerical examples.
{"title":"Control of Multiagent Networks With Misbehaving Nodes Over Directed Graph Topologies","authors":"Emre Yildirim;Tansel Yucelen","doi":"10.1109/TCNS.2025.3526569","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526569","url":null,"abstract":"In this article, we study multiagent networks over directed graph topologies involving nodes subject to exogenous disturbances (i.e., misbehaving nodes) and nodes that receive feedback control signals (i.e., driver nodes) for the purpose of suppressing the adverse effects of misbehaving nodes. The number of driver nodes can be less than the total number of nodes in the multiagent network. Specifically, we propose proportional–integral feedback controllers to be executed by driver nodes. These controllers guarantee the stability of the overall multiagent network in the sense of input-to-state stability (i.e., they make the resulting closed-loop system matrix Hurwitz). Furthermore, we utilize a graph-theoretical approach that allows users to find the steady-state values of critical nodes without requiring the knowledge of the Laplacian matrix of the overall multiagent network. The results presented in this article pave the way for understanding how driver nodes need to be selected to suppress the effect of misbehaving nodes on the neighborhood of critical nodes, which is further illustrated through illustrative numerical examples.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1521-1530"},"PeriodicalIF":4.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331614","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 : 2025-01-06DOI: 10.1109/TCNS.2025.3526327
Melih Bastopcu;S. Rasoul Etesami;Tamer Başar
In this work, we consider dynamic influence maximization games over social networks with multiple players (influencers). At the beginning of each campaign opportunity, individuals' opinion dynamics take independent and identically distributed realizations based on an arbitrary distribution. Upon observing the realizations, influencers allocate some of their budgets to affect their opinion dynamics. Then, individuals' opinion dynamics evolve according to the well-known DeGroot model. In the end, influencers collect their reward based on the final opinion dynamics. Each influencer's goal is to maximize their own reward subject to their limited total budget rate constraints, leading to a dynamic game problem. We first consider the offline and online versions of a single influencer's optimization problem where the opinion dynamics and campaign durations are either known or not known a priori. Then, we consider the game formulation with multiple influencers in offline and online settings. For the offline setting, we show that the dynamic game admits a unique Nash equilibrium policy and provide a method to compute it. For the online setting and with two influencers, we show that if each influencer applies the same no-regret online algorithm proposed for the single-influencer maximization problem, they converge to the set of $epsilon$-Nash equilibrium policies where $epsilon =mathcal {O}(1/sqrt{K})$ scales in average inversely with the number of campaign times $K$ considering the influencers' average utilities. Moreover, we extend this result to any finite number of influencers under more strict requirements on the information structure.
{"title":"Online and Offline Dynamic Influence Maximization Games Over Social Networks","authors":"Melih Bastopcu;S. Rasoul Etesami;Tamer Başar","doi":"10.1109/TCNS.2025.3526327","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526327","url":null,"abstract":"In this work, we consider dynamic influence maximization games over social networks with multiple players (influencers). At the beginning of each campaign opportunity, individuals' opinion dynamics take independent and identically distributed realizations based on an arbitrary distribution. Upon observing the realizations, influencers allocate some of their budgets to affect their opinion dynamics. Then, individuals' opinion dynamics evolve according to the well-known DeGroot model. In the end, influencers collect their reward based on the final opinion dynamics. Each influencer's goal is to maximize their own reward subject to their limited total budget rate constraints, leading to a dynamic game problem. We first consider the <italic>offline</i> and <italic>online</i> versions of a single influencer's optimization problem where the opinion dynamics and campaign durations are either known or not known a priori. Then, we consider the game formulation with multiple influencers in offline and online settings. For the offline setting, we show that the dynamic game admits a unique Nash equilibrium policy and provide a method to compute it. For the online setting and with two influencers, we show that if each influencer applies the same no-regret online algorithm proposed for the single-influencer maximization problem, they converge to the set of <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-Nash equilibrium policies where <inline-formula><tex-math>$epsilon =mathcal {O}(1/sqrt{K})$</tex-math></inline-formula> scales in average inversely with the number of campaign times <inline-formula><tex-math>$K$</tex-math></inline-formula> considering the influencers' average utilities. Moreover, we extend this result to any finite number of influencers under more strict requirements on the information structure.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 2","pages":"1440-1453"},"PeriodicalIF":4.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331536","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}
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