Ieee-Caa Journal of Automatica Sinica最新文献

Formation Control of Multi-Agent Systems With Position Constraints on a Closed Curve 封闭曲线上位置约束的多智能体系统编队控制

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2026-04-30 DOI: 10.1109/JAS.2025.125219

Cheng Song;Yongqin He;Jianbin Qiu;Shengyuan Xu

引用次数: 0

Distribution Network Partitioning for Voltage Regulation Using Heterogeneous Graph Neural Networks Considering Cyber-Attacks Risk 考虑网络攻击风险的异质图神经网络配电网调压分区

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2026-04-30 DOI: 10.1109/JAS.2026.125795

Lei Xu;Bo Zhang;Chunxia Dou;Dong Yue

引用次数: 0

Deep Reinforcement Learning Based on Search Space Independent Operators for Black-Box Continuous Optimization 基于搜索空间无关算子的深度强化学习黑盒连续优化

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2026-04-30 DOI: 10.1109/JAS.2025.125444

Ye Tian;Yisai Liu;Shangshang Yang;Xingyi Zhang

{"title":"Deep Reinforcement Learning Based on Search Space Independent Operators for Black-Box Continuous Optimization","authors":"Ye Tian;Yisai Liu;Shangshang Yang;Xingyi Zhang","doi":"10.1109/JAS.2025.125444","DOIUrl":"https://doi.org/10.1109/JAS.2025.125444","url":null,"abstract":"Deep reinforcement learning (DRL) has demonstrated exceptional capabilities in combinatorial optimization, which automatically devises policies for solution construction and optimizer refinement. DRL is particularly adept in generating training samples by itself, thereby providing the flexibility to solve a variety of combinatorial optimization problems without supervision. While DRL takes actions according to states extracted from problem-specific information, it cannot be directly applied to black-box continuous optimization lacking explicit information. To address this issue, this paper proposes a search space independent operator based DRL method for black-box continuous optimization. It conceptualizes the optimization process driven by search space independent operators as a Markov decision process, wherein actions are defined as operators and states are extracted from solutions generated by operators. In contrast to other DRL-assisted metaheuristics, the proposed method does not rely on any existing metaheuristic. Instead, it innovates by creating totally new operators, able to surpass the performance boundaries of existing metaheuristics. Compared with state-of-the-art meta-heuristics and DRL methods, the proposed method shows significantly faster convergence speed on challenging continuous optimization problems.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 4","pages":"913-925"},"PeriodicalIF":19.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147757150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Understanding Agentic AI: Algorithms and Infrastructure 理解人工智能：算法和基础设施

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2026-04-30 DOI: 10.1109/JAS.2026.125993

Wanlun Ma;Yongjian Guo;Qing-Long Han;Wei Zhou;Xiaogang Zhu;Junwu Xiong;Sheng Wen;Yang Xiang

{"title":"Understanding Agentic AI: Algorithms and Infrastructure","authors":"Wanlun Ma;Yongjian Guo;Qing-Long Han;Wei Zhou;Xiaogang Zhu;Junwu Xiong;Sheng Wen;Yang Xiang","doi":"10.1109/JAS.2026.125993","DOIUrl":"https://doi.org/10.1109/JAS.2026.125993","url":null,"abstract":"The rapid evolution of large language models (LLMs) towards autonomous Agentic artificial intelligence (AI) necessitates a systemic overhaul across algorithms, infrastructure, and architectures. This paper presents a unified view of the “Agentic AI Infrastructure,” connecting research threads often studied in isolation. First, post-training algorithms are reviewed, contrasting traditional reinforcement learning (RL) with emerging reasoning-centric methods and test-time scaling strategies. Next, the transition of RL training frameworks is analyzed from monolithic, colocated designs to disaggregated, asynchronous architectures tailored for the extreme variance of agentic rollouts. Furthermore, progress in agent construction is synthesized, covering reflection, planning, tool use, and multi-agent collaboration. By integrating these layers, the paper elucidates how agentic AI systems impose unique demands on underlying training systems. Finally, open challenges are outlined by covering capability scaling, efficiency, safety, privacy, and governance for reliable real-world agentic AI deployment.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 4","pages":"776-795"},"PeriodicalIF":19.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147757130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 Gain Scheduling Dynamic Event-Triggered Semi-Global Leader-Following Consensus of Input Constrained MASs Under Fixed/Switching Topologies 固定/交换拓扑下输入约束质量的分布式增益调度动态事件触发半全局先导跟随一致性

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2025-03-14 DOI: 10.1109/JAS.2025.125417

Meilin Li;Tieshan Li;Hongjing Liang

{"title":"Distributed Gain Scheduling Dynamic Event-Triggered Semi-Global Leader-Following Consensus of Input Constrained MASs Under Fixed/Switching Topologies","authors":"Meilin Li;Tieshan Li;Hongjing Liang","doi":"10.1109/JAS.2025.125417","DOIUrl":"https://doi.org/10.1109/JAS.2025.125417","url":null,"abstract":"In this paper, the semi-global leader-following consensus issue of multi-agent systems with constrained input under fixed and switching topologies is investigated via a distributed gain scheduling dynamic event-triggered method. First, a novel distributed gain scheduling consensus protocol is proposed under fixed topology, which integrates time-varying gain and distributed parameter schedulers. This approach enhances the transient performance of consensus tracking by enlarging the gain parameter through the scheduler, while the reliance of the scheduler on global state information is eliminated via a distributed design method. Subsequently, a distributed dynamic eventtriggered mechanism is introduced to reduce the controller updates, while the expression of the inter-event times mitigates its explicit reliance on the system matrix. Additionally, to eliminate the need for real-time monitoring of neighboring agents' states and continuous communication, a distributed dynamic self-triggered mechanism is developed. Next, our approaches are extended to solve the semi-global leader-following consensus problem under switching topologies. The average dwell time technique is employed to alleviate the limitations on the switching rate among multiple topologies. Finally, the theoretical analysis is validated through simulation results.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 4","pages":"888-902"},"PeriodicalIF":19.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147757169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Cyber-Physical Coordinated Bi-Level Active Power Control for Active Distribution Network Considering Transmission Congestion 考虑传输拥塞的有功配电网的信息物理协调双级有功控制

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2026-04-30 DOI: 10.1109/JAS.2025.125555

Bo Zhang;Dong Yue;Chunxia Dou;Dongmei Yuan;Lei Xu;Houjun Li

{"title":"Cyber-Physical Coordinated Bi-Level Active Power Control for Active Distribution Network Considering Transmission Congestion","authors":"Bo Zhang;Dong Yue;Chunxia Dou;Dongmei Yuan;Lei Xu;Houjun Li","doi":"10.1109/JAS.2025.125555","DOIUrl":"https://doi.org/10.1109/JAS.2025.125555","url":null,"abstract":"The degree of active power fluctuation is a key indicator for assessing the stability of active distribution networks. However, with the increasing clustering of distributed resources within these networks and the deepening integration of cyber-physical systems, uncertainties arising from cyber and physical domains, e.g., load variations and transmission congestion, will compound and exacerbate power fluctuations. Unlike existing methods that use cyber-physical cut-off control or firewall-based passive defenses, this paper proposes a bi-level active power control method based on a cyber-physical cooperation perspective to address these issues. At the upper level, which encompasses source-grid-storage clusters: in the physical layer, an active power support approach is proposed, which incorporates multi-factor matching while considering flow constraints to achieve multi-objective optimization regulation. In the cyber layer, we propose data sensitivity calculations along with demand-driven path planning techniques to ensure that planned paths align with regulatory requirements. At the lower level, focusing on in-cluster resources: in the physical layer, a multi-resource distributed control method based on fault-tolerance principles and a virtual leader-following consensus algorithm is proposed, which enables flexible responses to cluster commands while defending against light congestion interference. In the cyber layer, an eventtriggered path reconstruction method is proposed to defend against heavy congestion interference. The proposed methodology effectively harnesses the aggregation control capabilities of massive resources and facilitates an active defense against network congestion issues. Case studies show that these methods can generate optimal control commands for aggregators and internal resources within seconds to mitigate power fluctuations while ensuring reliable network performance in both planning and operational dimensions.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 4","pages":"837-853"},"PeriodicalIF":19.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147757137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Bounded Control Gain Based Prescribed-Time Consensus of General Linear Multi-Agent Systems With Controllable Agent Dynamics 具有可控智能体动态的一般线性多智能体系统的定时一致性有界控制增益

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2026-04-30 DOI: 10.1109/JAS.2025.125909

Hongpeng Li;Xinchun Jia;Xiaobo Chi;Yanpeng Guan

{"title":"Bounded Control Gain Based Prescribed-Time Consensus of General Linear Multi-Agent Systems With Controllable Agent Dynamics","authors":"Hongpeng Li;Xinchun Jia;Xiaobo Chi;Yanpeng Guan","doi":"10.1109/JAS.2025.125909","DOIUrl":"https://doi.org/10.1109/JAS.2025.125909","url":null,"abstract":"In this paper, the bounded control gain based pre-scribed-time (pre-T) consensus problem for general linear multi-agent systems (MASs) with controllable agent dynamics is addressed. First, an observer with Pre-T performance is designed for each agent to estimate the leader's state within a prescribed time. Then, based on the estimated states, a Pre-T switching controller integrating a bounded control gain is developed by employing a special coordinate transformation in combination with the backstepping technique, under the assumption that the agents' system matrix pair is controllable. It is shown that the proposed controller enables general linear MASs to achieve the Pre-T consensus independently of the agents' initial conditions and control parameters. Notably, the controller eliminates the numerical implementation problem associated with unbounded control gains, without compromising the consensus performance. The proposed approach is further applied to high-order single-input MASs to demonstrate its broader applicability. Finally, a simulation example validates the effectiveness of both the proposed observer and the Pre-T switching controller.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 4","pages":"903-912"},"PeriodicalIF":19.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147757128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Diversity-Driven Contrastive Value Ensembles with Categorical Constraints for Goal-Conditioned Robotic Control 目标条件机器人控制的分类约束的多样性驱动对比值集成

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2026-04-30 DOI: 10.1109/JAS.2025.125885

Zhiyi Shi;Ruihao Zhu;Shuai Wu;Wei Tong;Guangyu Zhu;Edmond Q. Wu

{"title":"Diversity-Driven Contrastive Value Ensembles with Categorical Constraints for Goal-Conditioned Robotic Control","authors":"Zhiyi Shi;Ruihao Zhu;Shuai Wu;Wei Tong;Guangyu Zhu;Edmond Q. Wu","doi":"10.1109/JAS.2025.125885","DOIUrl":"https://doi.org/10.1109/JAS.2025.125885","url":null,"abstract":"Dear Editor, This letter presents a contrastive reinforcement learning (Contrastive RL)-based framework, addressing challenging goal-conditioned problems in robotic control. While Contrastive RL offers promise in learning state-action-goal relationships, it suffers from a critical limitation: Insufficient discriminability between positive and negative samples attributed to inefficient value exploration and model overfitting. To overcome these challenges, the proposed algorithm extends Contrastive RL by leveraging an ensemble of critic networks to model state-action-goal alignment, alleviating the overfitting problem. Furthermore, the architecture introduces a dual component loss function: 1) A diversity-driven term to mitigate exploration redundancy in value estimation; and 2) A categorical-guidance constraint to ensure the discriminability capacity across contrasting pairs. We term this integrated framework diversity-driven contrastive value ensembles with categorical constraints (DiCE-CC). Experimental validation across three robotic manipulation scenarios demonstrates the effectiveness of the proposed algorithm in solving complex goal-conditioned control problems.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 4","pages":"1001-1003"},"PeriodicalIF":19.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11503199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147757161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Dynamic Robust Pursuit of Multiple Evaders Under State Measurement Uncertainty in Obstacle Environments 障碍环境下状态测量不确定性下多规避器动态鲁棒追踪

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2026-04-30 DOI: 10.1109/JAS.2025.125684

Kai Rao;Huaicheng Yan;Zhihao Huang;Tiantian Xu;Penghui Yang;Yunkai Lv

引用次数: 0

Support Vector Clustering Uncovered: Insights, Challenges, and Future Outlook 支持向量聚类揭示：洞察，挑战和未来展望

IF 19.2 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-04-01 Epub Date: 2026-04-30 DOI: 10.1109/JAS.2026.125804

M. Tanveer;Mohammad Tabish;Anuradha Kumari;Ashwani Kumar Malik;Weiping Ding

{"title":"Support Vector Clustering Uncovered: Insights, Challenges, and Future Outlook","authors":"M. Tanveer;Mohammad Tabish;Anuradha Kumari;Ashwani Kumar Malik;Weiping Ding","doi":"10.1109/JAS.2026.125804","DOIUrl":"https://doi.org/10.1109/JAS.2026.125804","url":null,"abstract":"Support vector clustering (SVC) has emerged as a powerful unsupervised learning technique, derived from support vector machines (SVMs), offering a robust solution to a wide range of complex clustering challenges. Its unique ability to handle noise, outliers, and clusters of diverse, irregular shapes sets it apart from traditional clustering methods. SVC's distinct advantage lies in its capacity to autonomously determine the optimal number of clusters without prior topological knowledge of the data. SVC maps data to a higher-dimensional space, encloses it in a minimal sphere, and identifies clusters when mapped back, supporting complex shapes and ensuring optimality through kernel functions. This review paper provides a comprehensive analysis of the SVC algorithms, exploring their variants such as robust, sparse, and fuzzy-based models and adaptations for large-scale data. Moreover, we analyze the potential of twin support vector clustering (TWSVC), with an emphasis on the use of various loss functions. Finally, the paper explores emerging trends and outlines promising future research directions for both SVC and twin SVC. These include advancements in feature engineering, extension to semi-supervised and weakly supervised learning, and the integration of multi-view and multimodal data. Our work aims to deepen the understanding of SVC, fostering advancements that address the evolving needs of clustering in real-world scenarios.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 4","pages":"749-775"},"PeriodicalIF":19.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147757153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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