IEEE Transactions on Control of Network Systems最新文献_第10页

Structural Controllability of Multiplex Networks With the Minimum Number of Driver Nodes 具有最少驱动节点数的多路复用网络的结构可控性

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-04 DOI: 10.1109/TCNS.2024.3372712

Xiang Li;Guoqi Li;Leitao Gao;Lock Yue Chew;Gaoxi Xiao

引用次数: 0

Distributed Algorithm for Constrained Social Cost Minimization Problem of Heterogeneous Linear Multiagent Systems Against DoS Attacks 针对 DoS 攻击的异构线性多代理系统受限社会成本最小化问题的分布式算法

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-04 DOI: 10.1109/TCNS.2024.3372889

Xiaohong Nian;Fan Li;Dongxin Liu

引用次数: 0

Distributed Nonconvex Optimization: Gradient-Free Iterations and $epsilon$-Globally Optimal Solution 分布式非凸优化：无梯度迭代和全球最优解

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-02 DOI: 10.1109/TCNS.2024.3395723

Zhiyu He;Jianping He;Cailian Chen;Xinping Guan

{"title":"Distributed Nonconvex Optimization: Gradient-Free Iterations and $epsilon$-Globally Optimal Solution","authors":"Zhiyu He;Jianping He;Cailian Chen;Xinping Guan","doi":"10.1109/TCNS.2024.3395723","DOIUrl":"10.1109/TCNS.2024.3395723","url":null,"abstract":"Distributed optimization utilizes local computation and communication to realize a global aim of optimizing the sum of local objective functions. This article addresses a class of constrained distributed nonconvex optimization problems involving univariate objectives, aiming to achieve global optimization without requiring local evaluations of gradients at every iteration. We propose a novel algorithm named Chebyshev-proxy-and-consensus-based algorithm, exploiting the notion of combining Chebyshev polynomial approximation, average consensus, and polynomial optimization. The proposed algorithm is able to obtain <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-globally optimal solutions for any arbitrarily small given accuracy <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>, efficient in both zeroth-order queries (i.e., evaluations of function values) and interagent communication, and distributed terminable when the specified precision requirement is met. The key insight is to use polynomial approximations to substitute for general local objectives, distribute these approximations via average consensus, and solve an easier approximate version of the original problem. Due to the nice analytic properties of polynomials, this approximation not only facilitates efficient global optimization, but also allows the design of gradient-free iterations to reduce cumulative costs of queries and achieve geometric convergence for solving nonconvex problems. We provide a comprehensive analysis of the accuracy and complexities of the proposed algorithm.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"11 4","pages":"2239-2251"},"PeriodicalIF":4.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834833","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}

引用次数: 0

Leaderless Consensus Control of Fractional-Order Nonlinear Multiagent Systems With Measurement Sensitivity and Actuator Attacks 具有测量灵敏度和执行器攻击的分数阶非线性多代理系统的无领导共识控制

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-02 DOI: 10.1109/TCNS.2024.3395721

Yang Liu;Xiangpeng Xie;Mohammed Chadli;Jiayue Sun

{"title":"Leaderless Consensus Control of Fractional-Order Nonlinear Multiagent Systems With Measurement Sensitivity and Actuator Attacks","authors":"Yang Liu;Xiangpeng Xie;Mohammed Chadli;Jiayue Sun","doi":"10.1109/TCNS.2024.3395721","DOIUrl":"10.1109/TCNS.2024.3395721","url":null,"abstract":"In this article, we investigate the leaderless consensus control for fractional-order multiagent systems (FOMASs), in which the sensors are subject to unknown measurement sensitivity and the actuators are subject to deception attacks, respectively. Due to the special nature of fractional calculus operations, the control design for FOMASs with the unknown sensitivity and attacks is more challenging. In order to solve these difficulties, some auxiliary variables are constructed deliberately with which the distributed adaptive control scheme is proposed. Meanwhile, we design a fractional-order filter to avoid the problem of “complexity explosion.” Compared with the existing filters, the proposed filter can confirm that the filter errors are asymptotically convergent and the unknown upper bound of fractional-order derivatives of virtual control signals is estimated effectively. From the rigorous theoretical analysis, it is proved that under the proposed adaptive control, the asymptotic consensus can be achieved in the presence of the sensors with unknown measurement sensitivity and actuator attacks. Finally, two examples are applied to verify the feasibility of the presented control scheme.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"11 4","pages":"2252-2262"},"PeriodicalIF":4.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140829279","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}

引用次数: 0

Distributed No-Regret Learning in Aggregative Games With Residual Bandit Feedback 有残余强盗反馈的聚合博弈中的分布式无悔学习

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-02 DOI: 10.1109/TCNS.2024.3395849

Wenting Liu;Peng Yi

{"title":"Distributed No-Regret Learning in Aggregative Games With Residual Bandit Feedback","authors":"Wenting Liu;Peng Yi","doi":"10.1109/TCNS.2024.3395849","DOIUrl":"10.1109/TCNS.2024.3395849","url":null,"abstract":"This article investigates distributed no-regret learning in repeated aggregative games with bandit feedback. The players lack an explicit model of the game and can only learn their actions based on the sole available feedback of payoff values. In addition, they cannot directly access the aggregate term that contains global information, while each player shares information with its neighbors without revealing its own strategy. We present a novel no-regret learning algorithm named distributed online gradient descent with residual bandit. In the algorithm, each player maintains a local estimate of the aggregate and adaptively adjusts its next action through the residual bandit mechanism and the online gradient descent method. We first provide regret analysis for aggregative games where the player-specific problem is convex, showing crucial associations between the regret bound, network connectivity, and game structure. Then, we prove that when the game is also strictly monotone, the action sequence generated by the algorithm converges to the Nash equilibrium almost surely. Finally, we demonstrate the algorithm performance through numerical simulations on the Cournot game.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"11 4","pages":"1734-1745"},"PeriodicalIF":4.0,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140829216","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}

引用次数: 0

Distributed Consensus Algorithm for Decision-Making in Multiagent Multiarmed Bandit 多代理多臂匪徒中决策的分布式共识算法

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-01 DOI: 10.1109/TCNS.2024.3395850

Xiaotong Cheng;Setareh Maghsudi

{"title":"Distributed Consensus Algorithm for Decision-Making in Multiagent Multiarmed Bandit","authors":"Xiaotong Cheng;Setareh Maghsudi","doi":"10.1109/TCNS.2024.3395850","DOIUrl":"10.1109/TCNS.2024.3395850","url":null,"abstract":"In this article, we study a structured multiagent multiarmed bandit (MAMAB) problem in a dynamic environment. A graph reflects the information-sharing structure among agents, and the arms' reward distributions are piecewise-stationary with several unknown change points. The agents face the identical piecewise-stationary MAB problem. The goal is to develop a decision-making policy for the agents that minimizes the regret, which is the expected total loss of not playing the optimal arm at each time step. Our proposed solution, restarted Bayesian online change point detection in cooperative upper confidence bound (RBO-Coop-UCB) algorithm, involves an efficient multiagent UCB algorithm as its core enhanced with a Bayesian change point detector. We also develop a simple restart decision cooperation that improves decision-making. Theoretically, we establish that the expected group regret of RBO-Coop-UCB is upper bounded by <inline-formula><tex-math>$mathcal {O}(KNMlog T + Ksqrt{MTlog T})$</tex-math></inline-formula>, where <inline-formula><tex-math>$K$</tex-math></inline-formula> is the number of agents, <inline-formula><tex-math>$M$</tex-math></inline-formula> is the number of arms, and <inline-formula><tex-math>$T$</tex-math></inline-formula> is the number of time steps. Numerical experiments on synthetic and real-world datasets demonstrate that our proposed method outperforms the state-of-the-art algorithms.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"11 4","pages":"2187-2199"},"PeriodicalIF":4.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834832","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}

引用次数: 0

Optimal DoS Attack Strategy for Cyber-Physical Systems via Energy Allocation 通过能量分配优化网络物理系统的 DoS 攻击策略

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-01 DOI: 10.1109/TCNS.2024.3372134

Zhuping Wang;Sheng Gao;Hao Zhang;Huaicheng Yan;Tenghui Li

引用次数: 0

Yet Another Self-Stabilizing Minimum Vertex Cover of a Network With Stochastic Stability 又一个具有随机稳定性的自稳定最小顶点覆盖网络

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-01 DOI: 10.1109/TCNS.2024.3395725

Jie Chen;Rongpei Zhou

{"title":"Yet Another Self-Stabilizing Minimum Vertex Cover of a Network With Stochastic Stability","authors":"Jie Chen;Rongpei Zhou","doi":"10.1109/TCNS.2024.3395725","DOIUrl":"10.1109/TCNS.2024.3395725","url":null,"abstract":"The vertex covering of a network is one of the well-known combinatorial optimization problems, and the focal point in the perspective of autonomous intelligent systems is to achieve the optimal covering solutions with distributed local information by the nodes (individual systems) themselves. In this article, we utilize a potential game for the vertex cover problem, whose solutions to the minimum value of its global objective function are the minimum vertex covering states of a network, and newly propose a self-stabilizing-parallel-game-based (SPG) distributed algorithm for each vertex (player) to learn (update) its strategy parallelly with the local information. Under the proposed SPG algorithm, we prove that only the solutions to the minimum value of the potential game's global objective function are stochastically stable, and the covering strategies of all the players will converge with probability one to a stochastically stable state, which is beyond the general Nash equilibrium of vertex covering games in the literature. Furthermore, we estimate the convergence rate of the proposed SPG algorithm, and extensive samples with numerical examples verify the effectiveness and superiority of the proposed SPG algorithm on a variety of representative complex networks with different scales and standard benchmarks.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"11 4","pages":"2226-2238"},"PeriodicalIF":4.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140834902","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}

引用次数: 0

Generalized Nash Equilibrium Seeking for Directed Nonsmooth Multicluster Games via a Distributed Lipschitz Algorithm 通过分布式 Lipschitz 算法寻求有向非光滑多集群博弈的广义纳什均衡

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-01 DOI: 10.1109/TCNS.2024.3372140

Yue Wei;Xianlin Zeng;Hao Fang;Yulong Ding;Shuxin Ding

引用次数: 0

Safe Pricing Mechanisms for Distributed Resource Allocation With Bandit Feedback 带 Bandit 反馈的分布式资源分配的安全定价机制

IF 4 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2024-03-01 DOI: 10.1109/TCNS.2024.3372143

Spencer Hutchinson;Berkay Turan;Mahnoosh Alizadeh

{"title":"Safe Pricing Mechanisms for Distributed Resource Allocation With Bandit Feedback","authors":"Spencer Hutchinson;Berkay Turan;Mahnoosh Alizadeh","doi":"10.1109/TCNS.2024.3372143","DOIUrl":"10.1109/TCNS.2024.3372143","url":null,"abstract":"In societal-scale infrastructures, such as electric grids or transportation networks, pricing mechanisms are often used as a way to shape users' demand in order to lower operating costs and improve reliability. Existing approaches to pricing design for safety-critical networks often require that users are queried beforehand to negotiate prices, which has proven to be challenging to implement in the real world. To offer a more practical alternative, we develop learning-based pricing mechanisms that require no input from the users. These pricing mechanisms aim to maximize the utility of the users' consumption by gradually estimating the users' price response over a span of <inline-formula><tex-math>$T$</tex-math></inline-formula> time steps (e.g., days) while ensuring that the infrastructure network's safety constraints that limit the users' demand are satisfied at all time steps. We propose two different algorithms for the two different scenarios when: the utility function is chosen by the central coordinator to achieve a social objective, and the utility function is defined by the price response under the assumption that the users are self-interested agents. We prove that both algorithms enjoy <inline-formula><tex-math>$tilde{mathcal {O}} (T^{2/3})$</tex-math></inline-formula> regret with high probability. We then apply these algorithms to demand response pricing for the smart grid and numerically demonstrate their effectiveness.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"11 4","pages":"2010-2021"},"PeriodicalIF":4.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140016923","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}

引用次数: 0