IEEE Systems Journal最新文献_第10页

Quantum-Causal Optimization for Decentralized EV Charging Management in Smart Grids 智能电网中分散电动汽车充电管理的量子因果优化

IF 4.9 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1109/JSYST.2025.3647516

Fei Teng;Yunpeng Gao;Jiangzhao Wang;Wei Zhang;Miao Wu;Keyue Zhuo

{"title":"Quantum-Causal Optimization for Decentralized EV Charging Management in Smart Grids","authors":"Fei Teng;Yunpeng Gao;Jiangzhao Wang;Wei Zhang;Miao Wu;Keyue Zhuo","doi":"10.1109/JSYST.2025.3647516","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3647516","url":null,"abstract":"The rapid adoption of electric vehicles (EVs) poses significant challenges to smart grids, including grid instability, inequitable resource allocation, and inefficiency in real-time scheduling under high renewable energy penetration. To address these limitations, this article proposes a quantum-causal adaptive optimization framework. First, a quantum-driven hierarchical Q-learning framework is designed to optimize the local scheduling and global coordination of charging stations (CSs), thereby improving decision-making efficiency. Second, a multiobjective optimization based on dynamic weights is developed, which adjusts the weights of the objective function in real-time to flexibly respond to environmental factors such as system load changes and charging demand fluctuations to achieve load balance and fairness. Finally, a causal quantum variable strategy is proposed to enhance adaptability and global optimality by identifying state variables with direct causal influence on decision outcomes. Experimental results demonstrate lower peak-to-valley ratio, improved load distribution balance, and faster convergence compared to conventional methods. The proposed framework provides a scalable and highly fair solution for large-scale EV comanagement in highly volatile grids, enhancing operational reliability and promoting the deep integration of quantum intelligence with energy systems.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"20 1","pages":"75-86"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508157","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

IEEE Systems Journal Publication Information IEEE系统期刊出版信息

IF 4.9 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2026-04-21 DOI: 10.1109/JSYST.2026.3681379

引用次数: 0

Global Consensus Tracking Control of Nonlinear Multiagent Systems Under Quantitative Performance Constraints: A Low-Complexity Approach 定量性能约束下非线性多智能体系统的全局一致跟踪控制：一种低复杂度方法

IF 4.9 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2026-03-03 DOI: 10.1109/JSYST.2026.3665383

Shuxing Xuan;Hongjing Liang;Choon Ki Ahn

{"title":"Global Consensus Tracking Control of Nonlinear Multiagent Systems Under Quantitative Performance Constraints: A Low-Complexity Approach","authors":"Shuxing Xuan;Hongjing Liang;Choon Ki Ahn","doi":"10.1109/JSYST.2026.3665383","DOIUrl":"https://doi.org/10.1109/JSYST.2026.3665383","url":null,"abstract":"This article explores global consensus tracking control for multiagent systems with unknown time-varying gains and nonlinearities, subject to quantitative performance constraints. The challenge lies in designing a controller that achieves global consensus without relying on the system's initial conditions while also ensuring the prescribed settling time and the convergence accuracy of synchronization errors. First, a concise, differentiable, piecewise continuous regulation function is proposed. The combination of this regulation function with error transformation addresses the singularity issue associated with initial conditions. Then, a piecewise performance function is also introduced to quantify both settling time and steady-state accuracy of synchronization errors. Integrating the regulation and performance functions yields a novel, low-complexity, robust method for enforcing quantitative performance constraints. This approach ensures global consensus under specified constraints and eliminates the need for nonlinear function approximation, parameter estimation, higher order derivative computation, or adaptive law design. Finally, the effectiveness of the proposed method is demonstrated through comparative simulations involving a planar robotic system.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"20 1","pages":"181-192"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512021","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 Interval Observer-Based Consensus Control for Discrete-Time Multiagent Systems 离散时间多智能体系统的分布式区间观测器一致性控制

IF 4.9 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2026-02-16 DOI: 10.1109/JSYST.2026.3660324

Jun Huang;Changjie Li;Thach Ngoc Dinh;Yueyuan Zhang

引用次数: 0

Consensus of Multiagent Systems With Input Delay and Communication Restriction: A Delay-Dependent Quantization Control Approach 具有输入延迟和通信限制的多智能体系统的一致性：一种时延相关的量化控制方法

IF 4.9 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2026-01-06 DOI: 10.1109/JSYST.2025.3647701

Zhengxin Wang;Yuhua Dou;Yang Cao;Min Xiao;Guo-Ping Jiang;Guanrong Chen

引用次数: 0

Multiagent Multitarget Search with Multi-Head Attention 基于多头关注的多智能体多目标搜索

IF 4.9 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2026-03-04 DOI: 10.1109/JSYST.2026.3663302

Huiqin Pei;Zilong Luo

{"title":"Multiagent Multitarget Search with Multi-Head Attention","authors":"Huiqin Pei;Zilong Luo","doi":"10.1109/JSYST.2026.3663302","DOIUrl":"https://doi.org/10.1109/JSYST.2026.3663302","url":null,"abstract":"In swarm drone applications, the problem of multitarget self-organizing search in unknown environments has demonstrated immense potential. In this problem, agents need to collaboratively search for multiple dynamic targets while only observing their immediate surroundings. However, the computational complexity and uncertainty in traditional multiagent search processes have been critical factors limiting their performance. Therefore, this article focuses on the multitarget search problem. First, a multiagent search environment including agents, targets, and obstacles is constructed. Subsequently, this article proposes a multiagent multitarget search method (MASOS) in complex obstacle environments. This method integrates the Actor–Critic reinforcement learning algorithm and multi-head attention for UAV swarm collaborative control, enabling agents to focus more on critical information such as other agents, targets, and obstacles during observation. It aims to enhance search, collaboration, and obstacle avoidance capabilities in multitarget self-organizing search tasks. Experimental results show that the MASOS method outperforms other commonly used multiagent reinforcement learning algorithms in terms of coordination strategies. In large-scale self-organizing search tasks, the capture success rate of the MASOS method is nearly 100%. Finally, By designing multitask scenario experiments, conducting comparative analyses combining multilayer perceptron and multi-head attention mechanism, and performing ablation studies on key modules, the effectiveness and interpretability of the MASOS method are verified from two aspects: Performance and decision-making logic.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"20 1","pages":"201-210"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510988","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

An Adaptive Control of Disruption Propagation via Susceptible-Infectious-Recovered Dynamics and Gossip Communication 基于敏感-感染-恢复动力学和闲谈通信的干扰传播自适应控制

IF 4.9 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2026-02-25 DOI: 10.1109/JSYST.2026.3662770

Yang Lu;Yonggang Liang;Yan Zheng;Wei Xiang

{"title":"An Adaptive Control of Disruption Propagation via Susceptible-Infectious-Recovered Dynamics and Gossip Communication","authors":"Yang Lu;Yonggang Liang;Yan Zheng;Wei Xiang","doi":"10.1109/JSYST.2026.3662770","DOIUrl":"https://doi.org/10.1109/JSYST.2026.3662770","url":null,"abstract":"The adaptive gossip-based consensus algorithms, stochastic optimal control frameworks, and integrated susceptible-infectious-recovered (SIR) dynamics modeling have been used as the basis for managing localized network disruptions in production systems. In the specific scenario of localized network disruption, the fixed-parameter approaches fail to dynamically integrate economic and epidemiological dynamics, resulting in suboptimal convergence and high consensus error. In this article, a distributed adaptive optimal tuning algorithm (DAOTA) is proposed that uses extended SIR dynamics with a gossip-based communication protocol within a Hamiltonian framework to minimize the global performance index. Our approach leverages real-time adaptive penalty updates and decentralized iterative optimization to achieve rapid exponential decay in consensus error while ensuring computational efficiency in high-dimensional networks. Experimental results demonstrate that our method reduces the consensus error from 0.020 to 0.005, the global performance index from 1400 to 950, and the convergence time from 150 to 60 s, thereby outperforming conventional techniques in terms of robustness and real-time responsiveness.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"20 1","pages":"157-168"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512022","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

Leader–Follower Output Consensus for Heterogeneous Nonlinear Multiagent Systems Under Event-Triggered Communication: A Gain-Based Estimator Method 事件触发通信下异构非线性多智能体系统的领导-随从输出一致性：一种基于增益的估计方法

IF 4.4 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2026-04-02 DOI: 10.1109/JSYST.2026.3670312

Wenjie Zhang;Yanan Qi;Xianfu Zhang

引用次数: 0

A Centralized-Distributed Hybrid Control Scheme for Voltage Control in Active Distribution Networks With High Renewable Penetration 高可再生电网电压控制的集中式分布式混合控制方案

IF 4.9 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2025-12-25 DOI: 10.1109/JSYST.2025.3643533

Mudaser Rahman Dar;Sanjib Ganguly

{"title":"A Centralized-Distributed Hybrid Control Scheme for Voltage Control in Active Distribution Networks With High Renewable Penetration","authors":"Mudaser Rahman Dar;Sanjib Ganguly","doi":"10.1109/JSYST.2025.3643533","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3643533","url":null,"abstract":"This article presents a hierarchical voltage control framework based on multitimescale model predictive control (MPC) for ADNs, with high penetration of photovoltaic systems and electric vehicles. The proposed scheme utilizes a hybrid control architecture, wherein OLTC is controlled at the first stage using MPC, leveraging sparse measurement and communication-based centralized control scheme. Inverter-driven control devices (IDCDs) are operated on a faster timescale, utilizing MPC-based distributed coordination. To enhance operational efficiency, a community detection algorithm based on network modularity is utilized for network clustering. A modified modularity metric incorporating the diameter of the cluster, in addition to the average voltage sensitivity and device regulation capacity, is utilized for improved network partitioning. A decomposition coordination control model is used for distributed control of IDCDs, utilizing the alternating-direction method of multipliers, with reduced communication overhead. The scalability and coordination capability of the presented model are validated on the 33-bus network and the IEEE 123-bus network.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"20 1","pages":"16-27"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507380","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

Fuzzy Logic-Based Adaptive Target Fencing Tracking of Uncertain Networked Agent Systems 基于模糊逻辑的不确定网络智能体自适应目标围栏跟踪

IF 4.9 3区计算机科学

IEEE Systems Journal Pub Date : 2026-03-01 Epub Date: 2026-01-01 DOI: 10.1109/JSYST.2025.3646717

Weihao Li;Mengji Shi;Ling Liu;Bowen Chen;Boxian Lin;Kaiyu Qin

{"title":"Fuzzy Logic-Based Adaptive Target Fencing Tracking of Uncertain Networked Agent Systems","authors":"Weihao Li;Mengji Shi;Ling Liu;Bowen Chen;Boxian Lin;Kaiyu Qin","doi":"10.1109/JSYST.2025.3646717","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3646717","url":null,"abstract":"Networked multi-uncrewed aerial vehicle (UAV) systems form the foundation of aerial Internet of Things applications. However, the presence of noncooperative or misbehaving agents, such as rogue UAVs, poses significant operational and coordination challenges. With this in mind, this article investigates the dynamic fencing control problem for networked agent systems in the presence of compound constraints, including model uncertainties and actuator faults. To this end, we propose a fuzzy logic-based robust fencing control scheme. First, fuzzy logic systems are employed to accurately approximate the unknown dynamics of the target, enhancing adaptability and prediction accuracy. Second, a resilient control strategy is developed to accommodate potential actuator faults, thereby mitigating their impact and ensuring closed-loop stability and desired control performance. Furthermore, a dual-sided fencing mechanism is introduced using signed graphs, which enables agents from different subgroups to coordinate from opposite directions, thereby forming an efficient and symmetric enclosure around the target. Finally, simulation results are provided to demonstrate the effectiveness of the proposed fencing control scheme.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"20 1","pages":"40-51"},"PeriodicalIF":4.9,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510314","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