IEEE Transactions on Control of Network Systems最新文献_第4页

Robo-Taxi Fleet Coordination at Scale via Reinforcement Learning 基于强化学习的机器人出租车车队协调

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-03-08 DOI: 10.1109/TCNS.2026.3691597

Luigi Tresca;Carolin Schmidt;James Harrison;Filipe Rodrigues;Gioele Zardini;Daniele Gammelli;Marco Pavone

{"title":"Robo-Taxi Fleet Coordination at Scale via Reinforcement Learning","authors":"Luigi Tresca;Carolin Schmidt;James Harrison;Filipe Rodrigues;Gioele Zardini;Daniele Gammelli;Marco Pavone","doi":"10.1109/TCNS.2026.3691597","DOIUrl":"https://doi.org/10.1109/TCNS.2026.3691597","url":null,"abstract":"Fleets of robo-taxis offering on-demand transportation services, commonly known as autonomous mobility-on-demand (AMoD) systems, hold significant promise for societal benefits, such as reducing pollution, energy consumption, and urban congestion. However, orchestrating these systems at scale remains a critical challenge, with existing coordination algorithms often failing to exploit the systems' full potential. This work introduces a novel decision-making framework that unites mathematical modeling with data-driven techniques. In particular, we present the AMoD coordination problem through the lens of reinforcement learning and propose a graph network-based framework that exploits the main strengths of graph representation learning, reinforcement learning, and classical operations research tools. Extensive evaluations across diverse simulation fidelities and scenarios demonstrate the flexibility of our approach, achieving superior system performance, computational efficiency, and generalizability compared to prior methods. Finally, motivated by the need to democratize research efforts in this area, we release publicly available benchmarks, datasets, and simulators for network-level coordination alongside an open-source codebase designed to provide accessible simulation platforms and establish a standardized validation process for comparing methodologies.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"13 3","pages":"1436-1448"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11512984","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985912","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}

引用次数: 0

Finite-Time Robust Optimization of Second-Order Multiagent Systems 二阶多智能体系统的有限时间鲁棒优化

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-03-08 DOI: 10.1109/TCNS.2026.3691601

Chenhong Zhu;Guanghui Wen;Dan Zhao;Xiao Fang;Tingwen Huang

{"title":"Finite-Time Robust Optimization of Second-Order Multiagent Systems","authors":"Chenhong Zhu;Guanghui Wen;Dan Zhao;Xiao Fang;Tingwen Huang","doi":"10.1109/TCNS.2026.3691601","DOIUrl":"https://doi.org/10.1109/TCNS.2026.3691601","url":null,"abstract":"This article studies two types of finite-time robust distributed optimization problems for second-order multiagent systems with disturbances. The first type of optimization problem focuses on handling consensus constraints, while the second type emphasizes solving supply–demand balance constraints. To address the adverse effects of external disturbances on the optimization process, this article proposes a two-stage optimization framework. The first stage employs an integral sliding-mode control strategy integrated with a supertwisting algorithm to reject unbounded disturbances, relaxing the restrictive assumption of bounded disturbances. The second stage implements two types of distributed finite-time controllers designed for two distinct optimization problems, respectively. One is designed for consensus optimization, employing homogeneous control and finite-time estimator-based techniques to achieve state consensus while collaboratively minimizing the global cost function; the other is developed for supply–demand balance optimization, utilizing a backstepping method to address the supply–demand balance constraint. Through rigorous theoretical analysis, it is proved that the proposed control strategies can effectively suppress external disturbances and enable the second-order multiagent system to converge to the optimal solutions of the optimization problems in finite time while ensuring stability. The proposed control strategies are validated through numerical simulations.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"13 3","pages":"1484-1496"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985629","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

Decentralized Parametric Stability Certificates for Grid-Forming Converter Control 网格成形变流器控制的分散参数稳定性证明

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-03-07 DOI: 10.1109/TCNS.2026.3691172

Verena Häberle;Xiuqiang He;Linbin Huang;Florian Dörfler;Steven Low

引用次数: 0

IEEE Control Systems Society Information IEEE控制系统学会信息

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-09-18 DOI: 10.1109/TCNS.2026.3731434

引用次数: 0

Generic Frameworks for Distributed Functional Optimization and Learning Over Time-Varying Networks 分布式函数优化和时变网络学习的通用框架

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-06-08 DOI: 10.1109/TCNS.2026.3701536

Zhan Yu;Zhongjie Shi;Deming Yuan;Daniel Wing Cheong Ho

{"title":"Generic Frameworks for Distributed Functional Optimization and Learning Over Time-Varying Networks","authors":"Zhan Yu;Zhongjie Shi;Deming Yuan;Daniel Wing Cheong Ho","doi":"10.1109/TCNS.2026.3701536","DOIUrl":"https://doi.org/10.1109/TCNS.2026.3701536","url":null,"abstract":"In this article, we establish a distributed functional optimization (DFO) theory over time-varying networks. The vast majority of existing distributed optimization theories are developed based on Euclidean decision variables. However, for many scenarios in machine learning and statistical learning, such as reproducing kernel spaces or probability measure spaces, where functions or probability measures serve as the fundamental variables, the development of existing distributed optimization theories exhibits obvious theoretical and technical deficiencies. To address these issues, we develop a novel general DFO theory on Banach spaces, allowing functional learning problems in the aforementioned scenarios to be incorporated into our framework for resolution. We study both convex and nonconvex DFO problems. We propose a distributed functional mirror descent algorithm and a distributed functional gradient descent algorithm to solve them. A comprehensive convergence theory of these methods is rigorously established, and satisfactory convergence rates are fully derived. The work provides generic analytical frameworks for DFO. The established theory is shown to have crucial application value in kernel-based distributed learning theory over time-varying networks.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"13 3","pages":"1771-1783"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985680","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 Kalman Filter With Augmented Measurement Fusion Under Time-Varying Graphs 时变图下增广测量融合的分布式卡尔曼滤波

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-03-08 DOI: 10.1109/TCNS.2026.3691477

Tuo Yang;Jiachen Qian;Zhisheng Duan;Ling Shi

引用次数: 0

Analysis of a Discontinuous Tracking System for Closest-Target Detection 一种用于最接近目标检测的不连续跟踪系统分析

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-06-16 DOI: 10.1109/TCNS.2026.3704391

Hyunjin Ahn;Woojoo Shim

{"title":"Analysis of a Discontinuous Tracking System for Closest-Target Detection","authors":"Hyunjin Ahn;Woojoo Shim","doi":"10.1109/TCNS.2026.3704391","DOIUrl":"https://doi.org/10.1109/TCNS.2026.3704391","url":null,"abstract":"In this article, we study a multiagent system governed by a state-dependent discontinuous vector field arising from closest-target selection dynamics. Unlike conventional approaches that regularize the target-assignment rule, we directly incorporate the closest-target selection rule into the continuous-time dynamics and analyze the resulting discontinuities within Filippov’s differential inclusion framework. The main analytical challenge stems from the nonsmooth relaxation force induced by target switching, which prevents the direct application of classical Lyapunov methods. To address this difficulty, we construct a Lyapunov functional compatible with Filippov set-valued derivatives and apply a LaSalle-type invariance principle for differential inclusions. A key step of the analysis is the explicit characterization of the Filippov set-valued relaxation force as the convex hull of admissible closest-target directions. Under verifiable conditions on the initial data, system parameters, and kernel functions, we establish global existence and asymptotic convergence of all Filippov solutions. In particular, we show that each tracking agent converges to a point in a finite set consisting of points that lie in the convex hull of the targets closest to those points, while all velocities decay to zero. This finding provides the strongest asymptotic convergence result that can be expected for discontinuous closest-target selection dynamics. The theoretical results are further illustrated by numerical simulations highlighting geometric obstructions in multitarget configurations.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"13 3","pages":"1797-1808"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985905","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

Model-Free Dynamic Consensus in Multiagent Systems: A $Q$-Function Perspective 多智能体系统的无模型动态一致性：$Q$-函数视角

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-03-19 DOI: 10.1109/TCNS.2026.3694729

Maryam Babazadeh;Naim Bajcinca

{"title":"Model-Free Dynamic Consensus in Multiagent Systems: A $Q$-Function Perspective","authors":"Maryam Babazadeh;Naim Bajcinca","doi":"10.1109/TCNS.2026.3694729","DOIUrl":"https://doi.org/10.1109/TCNS.2026.3694729","url":null,"abstract":"This article presents a new method for dynamic consensus in linear discrete-time homogeneous multiagent systems. Achieving state consensus in such systems involves constraints linked to the graph's spectral properties, complicating the design of coupling gains, especially in large-scale networks. The proposed approach reformulates the dynamic consensus problem with a prescribed convergence rate by introducing a state–action value function within a synthetic linear–quadratic regulation framework, thereby expressing the problem as a semidefinite program (SDP). The resulting SDP supports the joint design of local feedback and coupling gains in both model-based and model-free settings. To handle nonconvex feasibility conditions, a convex–concave decomposition strategy is developed, guaranteeing convergence to a stationary point. In the fully model-free case, the method eliminates the need for system identification or explicit knowledge of agent dynamics, relying solely on input-state data to construct an equivalent data-driven SDP. Finally, a new algorithm that balances feasibility, convergence rate, and energy efficiency enhances design flexibility. Numerical results demonstrate the effectiveness of the proposed method in diverse scenarios.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"13 3","pages":"1626-1638"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985432","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

Finite-Time Distributed Optimization of Nonlinear Multiagent Systems: A Small-Gain Theorem 非线性多智能体系统的有限时间分布优化：一个小增益定理

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-03-07 DOI: 10.1109/TCNS.2026.3691228

Zhanxiu Wang;Zhenghong Jin;Hua Li;Fengjun Xiao

{"title":"Finite-Time Distributed Optimization of Nonlinear Multiagent Systems: A Small-Gain Theorem","authors":"Zhanxiu Wang;Zhenghong Jin;Hua Li;Fengjun Xiao","doi":"10.1109/TCNS.2026.3691228","DOIUrl":"https://doi.org/10.1109/TCNS.2026.3691228","url":null,"abstract":"In this article, the finite-time distributed optimization problem is investigated for nonlinear multiagent systems under the quasi-strongly connected and weight-balanced graph. The main assignment is to drive all agents to an optimal solution of a specified global objective function, which is viewed as the referenced output, in finite-time, using the partial actual-output-related knowledge of the local information function. In order to achieve this assignment, finite-time adaptive distributed optimal coordinators and finite-time tracking controllers are designed separately. Exploiting the actual outputs of agents, the adaptive distributed optimal coordinators generate reference outputs that converge to the optimal solutions of the global objective function in finite-time, while the finite-time tracking schemes are developed to drive all output signals of each agent to track the reference outputs also in finite-time. Thus, the entire closed-loop system can be perceived as an interconnected system involving the two design modules mentioned above and nonlinear multiagent systems. The timescale separation approach, finite-time input-to-state stability and small-gain approach are applied to deal with the module of tracking controllers, distributed optimal coordinators and the entire interconnected closed-loop system, respectively. Finally, the effectiveness and superiority of the proposed algorithm are demonstrated by a numerical simulation.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"13 3","pages":"1296-1307"},"PeriodicalIF":5.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985566","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

Sparse Output Feedback Design for a Descriptor-Form Networked Dynamic System Ensuring Impulse-Freeness 一种保证无脉冲的描述符形式网络动态系统的稀疏输出反馈设计

IF 5.3 3区计算机科学

IEEE Transactions on Control of Network Systems Pub Date : 2026-09-01 Epub Date: 2026-03-12 DOI: 10.1109/TCNS.2026.3691759

Yuyu Zhou;Tong Zhou

引用次数: 0