IEEE Transactions on Cybernetics最新文献_第6页

Decentralized Output Feedback Control for Interconnected Nonlinear Systems Under Network Constraints via a Dynamic Gain Design Approach. 基于动态增益设计的网络约束下互联非线性系统分散输出反馈控制。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-27 DOI: 10.1109/TCYB.2026.3721198

Jiongzhi Qiu, Kuo Li, Yiming Fang, Changchun Hua

{"title":"Decentralized Output Feedback Control for Interconnected Nonlinear Systems Under Network Constraints via a Dynamic Gain Design Approach.","authors":"Jiongzhi Qiu, Kuo Li, Yiming Fang, Changchun Hua","doi":"10.1109/TCYB.2026.3721198","DOIUrl":"10.1109/TCYB.2026.3721198","url":null,"abstract":"This article investigates decentralized event-triggered dynamic output feedback adaptive fuzzy control for interconnected nonlinear systems under injection attacks and input quantization. Different from previous works, we propose a reduced-order dynamic observer-based event-triggered quantized control method under a relaxed Lipschitz condition and output injection attack, which effectively reduces computational burden and communication resource usage. First, a novel reduced-order dynamic gain observer estimates unmeasured states under injection attacks. Subsequently, to guarantee that each subsystem's output satisfies the prescribed performance requirements, a barrier-function-based tracking error mapping approach is proposed. Then, an adaptive fuzzy control scheme incorporating decentralized event-triggered output feedback and input quantization is developed using the backstepping technique, thereby optimizing energy efficiency in signal transmission. Next, Lyapunov analysis proves that all closed-loop signals remain bounded with Zeno behavior excluded. Finally, simulations validate the theoretical results under various attack scenarios.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148840077","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

Neural Value Alignment: Human-AI Collaboration Under Goal-Action Ambiguity. 神经价值对齐：目标-行动模糊下的人类-人工智能协作。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-24 DOI: 10.1109/TCYB.2026.3722605

Xin Xu, Yansen Wang, Dongqi Han, Dongsheng Li, Sang Wan Lee

{"title":"Neural Value Alignment: Human-AI Collaboration Under Goal-Action Ambiguity.","authors":"Xin Xu, Yansen Wang, Dongqi Han, Dongsheng Li, Sang Wan Lee","doi":"10.1109/TCYB.2026.3722605","DOIUrl":"10.1109/TCYB.2026.3722605","url":null,"abstract":"Value alignment plays a crucial role in human-artificial intelligence (AI) collaboration. Traditional approaches attempt to infer human goals from actions to guide AI policies. However, this behavior-level alignment faces an inherent challenge: the ambiguous mapping between goals and actions, as a single action might serve multiple possible goals, while different actions could achieve the same goal. To overcome these limitations, we propose neural value alignment (NVA), a unifying perspective that leverages key variables in human reinforcement learning (RL): reward prediction error (RPE) and state prediction error (SPE). RPE captures outcome discrepancies, refining AI goal inference, while SPE reflects state transition misalignment, shaping AI actions. Using a novel task paradigm that dissociated RPE and SPE, combined with electroencephalography (EEG) recordings, we demonstrated cortical decodability of RPE, SPE, and their co-occurrence, robustly across contexts. Simulations showed that RPE-SPE synergy accelerated value alignment, even under imperfect decoding. This study bridges RL and human-AI interaction, showing that RPE-SPE synergy enables flexible and human-compatible artificial systems operating under goal-action ambiguity.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812692","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

Energy-Constrained Optimal Stealthy Attacks Under Sequential State Estimation and Detection. 序列状态估计与检测下的能量约束最优隐身攻击。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-21 DOI: 10.1109/TCYB.2026.3722687

Hua-Sheng Shan, Ping Sun, Yi-Gang Li, Xiu-Xiu Ren, Xuan Liu

{"title":"Energy-Constrained Optimal Stealthy Attacks Under Sequential State Estimation and Detection.","authors":"Hua-Sheng Shan, Ping Sun, Yi-Gang Li, Xiu-Xiu Ren, Xuan Liu","doi":"10.1109/TCYB.2026.3722687","DOIUrl":"10.1109/TCYB.2026.3722687","url":null,"abstract":"This article investigates the stealthy attack design against the sequential estimation of cyberphysical systems (CPSs), with the aim of providing a theoretical basis for refining defense mechanisms. A multiobjective attack model is proposed under the energy constraint, which comprehensively utilizes available innovations from both reliable and suspicious sensors to disrupt the state estimation performance with the assurance of stealthiness. By deriving the recursion of the error covariance, the optimization objective is formulated as a tradeoff between the terminal and average estimation errors to accommodate different scenarios. The corresponding stealthiness conditions are further analyzed based on an analysis of the statistical characteristics of coupled innovations. Then, according to the properties of orthogonal matrices and the Lagrange multiplier method, closed-form solutions for the optimal attack matrices are derived, which avoid addressing the semidefinite programming problem. Moreover, the optimal scheduling is solved via 0-1 programming after parameter separation. Finally, numerical simulations are provided to validate the results.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148792415","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

Dual-Channel Event-Triggered Scaled Consensus for Multiagent Systems on Time Scales Under DoS Attacks DoS攻击下多智能体系统时间尺度双通道事件触发尺度一致性

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-20 DOI: 10.1109/tcyb.2026.3722904

Yin Sheng, Ruoyang Dang, Wei Tang, Zhigang Zeng, Tingwen Huang

引用次数: 0

Optimal Stealthy Attack Design for Distributed Consensus State Estimation: A Historical Innovation-Based Approach 分布式共识状态估计的最优隐身攻击设计：一种基于历史的创新方法

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-20 DOI: 10.1109/tcyb.2026.3721072

Chao Ge, Binghao Li, Yajuan Liu, Changchun Hua

引用次数: 0

Deep Reinforcement Learning for Communication-Free Distributed Control of Autonomous Vehicles in Unstructured Intersection 基于深度强化学习的非结构化交叉口自动驾驶车辆无通信分布式控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-19 DOI: 10.1109/tcyb.2026.3723789

Ruihao Zeng, Mohsen Ramezani

引用次数: 0

Pseudoinverse Fuzzy Control of Discrete-Time Time-Delay Nonlinear Systems With Butterfly Hysteresis and Its Application on BDEA-Based Motion Platform 具有蝶形滞后的离散时滞非线性系统的伪逆模糊控制及其在基于bdea的运动平台上的应用

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-19 DOI: 10.1109/tcyb.2026.3722834

Zhengyan Hu, Xiuyu Zhang, Xinkai Chen, Chun-Yi Su, Junjie Xu

引用次数: 0

Distributed Online Push-Sum Dual Averaging for Composite Optimization With Communication Delays 具有通信延迟的组合优化的分布式在线推和双平均

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-19 DOI: 10.1109/tcyb.2026.3722661

Cong Wang, Deming Yuan, Qian Ma, Jing Zhao

引用次数: 0

Secure Consensus of Multiagent Systems Under Replay Attacks: An Adaptive Parallel-Triggering Asynchronous Control Strategy 重放攻击下多智能体系统的安全一致性：一种自适应并行触发异步控制策略

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-18 DOI: 10.1109/tcyb.2026.3721158

Jinyuan Zhang, Xiaoyuan Luo, Xiaolei Li, Xinping Guan

引用次数: 0

Fuzzy Neural Module Network: Leveraging Univariate Models and Layer-Specific and Network-Wide Dual Learning 模糊神经模块网络：利用单变量模型和特定层和网络范围的双重学习

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-18 DOI: 10.1109/tcyb.2026.3717170

Eun-Hu Kim, Hao Huang, Zheng Wang, Haibin Duan, Zunwei Fu, Witold Pedrycz

引用次数: 0