IEEE Transactions on Cybernetics最新文献_第9页

A Fully Decentralized Online Actor-Critic Algorithm for Constrained Multiagent Reinforcement Learning Over Directed Graphs. 有向图上约束多智能体强化学习的完全分散在线Actor-Critic算法。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-05 DOI: 10.1109/TCYB.2026.3715812

Zirui Jiao, Wenwu Yu, He Wang

引用次数: 0

Fault Decoupling Through State Estimation Using Observer-Based Physics-Informed Neural Network. 基于观测器的物理信息神经网络状态估计故障解耦。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-05 DOI: 10.1109/TCYB.2026.3718151

Vijayakumar Muthusamy, Ines Chihi

{"title":"Fault Decoupling Through State Estimation Using Observer-Based Physics-Informed Neural Network.","authors":"Vijayakumar Muthusamy, Ines Chihi","doi":"10.1109/TCYB.2026.3718151","DOIUrl":"10.1109/TCYB.2026.3718151","url":null,"abstract":"In the context of fault diagnosis in smart manufacturing, fault decoupling has become a critical component, enabling systems to distinguish between various sources of anomalies, such as actuator faults, sensor faults, and external disturbances. This article proposes a joint state and fault estimation for linear time-invariant (LTI) systems affected by multiple faults and disturbances. First, the design of dual proportional-integral fading unknown input observers (PIFUIOs) is implemented to estimate the system states and fault signals under a structurally decoupled framework. Necessary and sufficient conditions are derived to guarantee the existence of the observers and to ensure mutual decoupling between faults and disturbances. Next, a physics-informed neural network (PINN) is framed, in which the loss functions are constructed based on the observer design to enhance estimation accuracy and incorporate physical knowledge of the observer into the learning process. In addition, sufficient conditions based on Lyapunov stability theory to ensure asymptotic convergence and attenuation of faults and disturbances are formulated using linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed approach is validated via a DC motor model.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148678252","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

Prescribed-Time Performance-Based Data-Driven Control of Permanent Magnet Linear Motors: A Resilient Hierarchical Approach. 基于规定时间性能的永磁直线电机数据驱动控制：一种弹性分层方法。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-05 DOI: 10.1109/TCYB.2026.3716721

Yuan-Xin Li, Wen Tan, Zhongsheng Hou

{"title":"Prescribed-Time Performance-Based Data-Driven Control of Permanent Magnet Linear Motors: A Resilient Hierarchical Approach.","authors":"Yuan-Xin Li, Wen Tan, Zhongsheng Hou","doi":"10.1109/TCYB.2026.3716721","DOIUrl":"10.1109/TCYB.2026.3716721","url":null,"abstract":"This article investigates coordinated velocity tracking control for heterogeneous permanent magnet linear motors (PMLMs) under actuator faults and denial-of-service (DoS) attacks. Parameter heterogeneity, actuator degradation, and communication failures jointly threaten system stability and tracking accuracy. A prescribed-time performance-based hierarchical resilient adaptive data-driven control strategy is proposed to solve the formulated issue. First, a distributed resilient prescribed-time observer is designed to estimate the time-varying virtual reference velocity despite intermittent communication failures caused by DoS attacks. Subsequently, using the dynamic linearization (DL) method, the ideal controller is converted into an equivalent data model that relies solely on input/output (I/O) data, bypassing the need for accurate PMLM models. By introducing a global prescribed-time performance function (PTPF), the constrained velocity tracking errors are transformed into equivalent unconstrained conditions. Based on the estimated reference signal, a decentralized controller-dynamic-linearization (CDL)-based fuzzy adaptive tracking controller is designed, in which the unknown fault-related nonlinear term is approximated using a fuzzy logic system to compensate for actuator fault effects. The prescribed-time stabilization of the observation error and velocity tracking error is validated by Lyapunov stability analysis and contraction mapping techniques. Finally, simulation examples confirm the effectiveness of the proposed scheme.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148678318","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

MC-SNN: Multicenter Stochastic Neural Network for Adversarially Robust Learning MC-SNN：对抗鲁棒学习的多中心随机神经网络

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-04 DOI: 10.1109/tcyb.2026.3717080

Meng Hu, Ran Wang, Yanting Guo, Xizhao Wang, Sam Kwong

引用次数: 0

Sliding Mode Control for 2-D FMII Networked System Under Partially Known Fading Channel Information 衰落信道信息部分已知的二维FMII网络系统滑模控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-04 DOI: 10.1109/tcyb.2026.3716786

Zheng Zhou, Guangchen Zhang, Shuping He, Xudong Zhao, Yuanqing Xia, Fengjuan Zhu

引用次数: 0

Diffusion-Based Knowledge Transfer for Multitask Optimization. 基于扩散的多任务优化知识转移。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-03 DOI: 10.1109/TCYB.2026.3711601

Jiang-Tao Chen, Zi-Jia Wang, Wei-Jie Yu, Tian-Fang Zhao, Zhi-Hui Zhan, Sam Kwong, Jun Zhang

{"title":"Diffusion-Based Knowledge Transfer for Multitask Optimization.","authors":"Jiang-Tao Chen, Zi-Jia Wang, Wei-Jie Yu, Tian-Fang Zhao, Zhi-Hui Zhan, Sam Kwong, Jun Zhang","doi":"10.1109/TCYB.2026.3711601","DOIUrl":"10.1109/TCYB.2026.3711601","url":null,"abstract":"Evolutionary multitask optimization (EMTO) aims to optimize multiple tasks simultaneously by transferring the related knowledge between tasks. Therefore, knowledge transfer (KT) between different tasks is crucial for facilitating the optimization of tasks. The traditional KT methods in EMTO achieve superficial KT through individual transfers, limiting their ability to transfer high-quality knowledge from other tasks. Therefore, in this article, a diffusion model-based KT (DMKT) method built specifically on cold diffusion (CD) is proposed to deeply mine the mapping relationships between different tasks and obtain transfer models for mapping individuals across tasks. Since CD supports arbitrary degradation operators, the interpolation between fitness-paired source and target task individuals can be defined as a task-oriented gradual degradation process, enabling the corresponding restoration process to learn directed cross-task mappings. In particular, we first construct two cyclic training diffusion models (DMs) for each source-target task pair. Subsequently, the trained DM can be used to generate new promising solutions for achieving efficient KT. The experimental results on the CEC2022 multitask optimization problem (MTOP) benchmark demonstrate that the proposed diffusion-based KT multitask optimization (DKTMTO) algorithm outperforms other state-of-the-art EMTO algorithms. Moreover, DMKT can be integrated into other EMTO algorithms to further improve their performance. Finally, DKTMTO is applied to real-world multitask planar kinematic arm control problems (PKACPs) and the WCCI2020 many-task optimization problem (MaTOP) benchmark, demonstrating its applicability and scalability.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148669282","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

Stochastic Watermarking Protection Strategy for CPSs Under Linear Stealthy Attacks. 线性隐身攻击下cps的随机水印保护策略

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-03 DOI: 10.1109/TCYB.2026.3716941

Pengyu Li, Dan Ye, Fang Wei

引用次数: 0

Fixed-Time Fault-Tolerant Control for Wastewater Treatment Processes With Asymmetric State Constraints 非对称状态约束下污水处理过程的定时容错控制。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-08-01 Epub Date: 2026-04-21 DOI: 10.1109/TCYB.2026.3683543

Hong-Gui Han;Han-Qian Hou;Hao-Yuan Sun;Jun-Fei Qiao

引用次数: 0

Reputation-Aware Distributed Filtering for Nonlinear Bias-Corrupted Systems Over Sensor Networks Under Event-Triggered Mechanism 事件触发机制下传感器网络非线性偏损系统的声誉感知分布式滤波