IEEE Transactions on Cybernetics最新文献_第5页

Principal Predictor Analysis With Application to Dynamic Process Monitoring. 主预测分析及其在动态过程监控中的应用。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-25 DOI: 10.1109/tcyb.2025.3610475

Shumei Chen,S Joe Qin

引用次数: 0

Adaptive Backstepping Control for Nonlinear Vehicles With Guaranteed String Stability and Suppressed Cascade Fluctuations. 具有保证串稳定性和抑制串级波动的非线性车辆自适应反步控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-25 DOI: 10.1109/tcyb.2025.3610436

Zhizhong Bai,Xiaoyuan Luo,Mengjie Li,Jiange Wang,Xinping Guan

{"title":"Adaptive Backstepping Control for Nonlinear Vehicles With Guaranteed String Stability and Suppressed Cascade Fluctuations.","authors":"Zhizhong Bai,Xiaoyuan Luo,Mengjie Li,Jiange Wang,Xinping Guan","doi":"10.1109/tcyb.2025.3610436","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3610436","url":null,"abstract":"Recent efforts have yielded substantial progress in backstepping platoon control for connected and automated vehicles (CAVs). While most existing studies focus on guaranteeing individual vehicle stability and string stability, their deployment in nonlinear vehicle platoons may face challenges from the so-called \"butterfly effect.\" That is, even with guaranteed string stability, potential instantaneous spacing changes may imply unpredictable, uncomfortable fluctuations in vehicular velocity and acceleration. To address this issue, a parallel error-fluctuation suppression control framework is proposed in this work. Specifically, tunable triple-layered error boundaries (i.e., spacing, velocity, and acceleration) are constructed to reactively confine all propagated errors within predefined envelopes. By integrating a Barbalat-lemma-enhanced filtering-compensating mechanism and an adaptive approach based on the approximation capability of radial basis function neural networks (RBFNNs), asymptotic error tracking is realized to proactively suppress potential fluctuations. An adaptive backstepping control approach-integrating proactive and reactive suppression strategies-is then proposed to mitigate the unquantifiable \"butterfly effect.\" Theoretical analysis and simulations demonstrate the validity and superiority of the proposed approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"15 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140098","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

Error Estimation for Quasi-Synchronization of Multilayer Dynamical Networks: A Pinning Delayed Impulsive Control Scheme. 多层动态网络准同步误差估计：一种固定延迟脉冲控制方案。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-25 DOI: 10.1109/tcyb.2025.3611096

Shiyu Dong,Jing Liang,Kaibo Shi,Mingyuan Yu,Jinde Cao,Huaicheng Yan

{"title":"Error Estimation for Quasi-Synchronization of Multilayer Dynamical Networks: A Pinning Delayed Impulsive Control Scheme.","authors":"Shiyu Dong,Jing Liang,Kaibo Shi,Mingyuan Yu,Jinde Cao,Huaicheng Yan","doi":"10.1109/tcyb.2025.3611096","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3611096","url":null,"abstract":"In this article, we address the error estimation problem of quasi-synchronization for a class of multilayer dynamical networks. The proposed network model simultaneously accounts for interlayer and intralayer time-varying coupling structures, network directionality, and interlayer communication delays. To achieve synchronization in a cost-effective manner, we design a novel pinning impulsive control strategy that leverages large-scale impulse delay information together with the number of pinned nodes. By employing an iterative algorithm, we establish a new delay-dependent impulsive differential inequality, which precisely characterizes the convergence domain and provides flexibility in the choice of impulse delays. Then, some quasi-synchronization criteria are derived to guarantee convergence of multilayer networks within a prescribed error level, and explicit analytical expressions for the synchronization error bounds are obtained. Finally, to demonstrate the practical applicability, the proposed criteria are applied to the synchronization of multilayer single-link robot arm networks under error bounds, with numerical examples validating the effectiveness of the method.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"100 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140215","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

Integrating Deep Model-Based Learning With Modular State-Based Stackelberg Games for Self-Optimizing Distributed Production Systems. 基于深度模型的学习与基于模块化状态的Stackelberg博弈的自优化分布式生产系统集成。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-25 DOI: 10.1109/tcyb.2025.3610707

Steve Yuwono,Andreas Schwung,Dorothea Schwung

{"title":"Integrating Deep Model-Based Learning With Modular State-Based Stackelberg Games for Self-Optimizing Distributed Production Systems.","authors":"Steve Yuwono,Andreas Schwung,Dorothea Schwung","doi":"10.1109/tcyb.2025.3610707","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3610707","url":null,"abstract":"This article introduces a novel integration of deep model-based learning with modular state-based Stackelberg games (Mod-SbSG) for distributed self-optimization in manufacturing systems, using a sample-efficient approach. Model-free Mod-SbSG requires frequent interactions with real systems to find optimal solutions, which can be costly, time-consuming, and risky in industrial settings. Prior studies handled this by using digital representations to train Mod-SbSG players, but accurate representations are often difficult to develop. Hence, our framework replaces digital representations with deep learning methods that learn system dynamics, optimize policies within Mod-SbSG, and reduce real-world interactions. The method includes two main steps: 1) designing deep learning models to predict system dynamics and 2) training Mod-SbSG players in virtual environments. We evaluate single-and multistep predictors and demonstrate network reuse for transfer learning in adaptable systems, which reduces real system interactions by 77.78% in a laboratory testbed industrial control scenario.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"18 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140213","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

Sliding Flexible Prescribed Performance Boundary-Guided Reinforcement Learning Control for Input-Constrained Nonlinear Systems. 输入约束非线性系统的滑动柔性规定性能边界引导强化学习控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-25 DOI: 10.1109/tcyb.2025.3610913

Yangang Yao,Ziyi Liu,Yu Kang,Yunbo Zhao,Jieqing Tan,Lichuan Gu,Qiang Li,Jinling Wang

{"title":"Sliding Flexible Prescribed Performance Boundary-Guided Reinforcement Learning Control for Input-Constrained Nonlinear Systems.","authors":"Yangang Yao,Ziyi Liu,Yu Kang,Yunbo Zhao,Jieqing Tan,Lichuan Gu,Qiang Li,Jinling Wang","doi":"10.1109/tcyb.2025.3610913","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3610913","url":null,"abstract":"This article first proposes a sliding flexible prescribed performance boundary-guided reinforcement learning (SFPPB-RL) control approach for input-constrained nonlinear systems (ICNSs). By designing a sliding flexible PPB, which not only can adaptively adjust the initial boundary according to the initial error, but also dynamically adjust the constraint relaxation according to the coupling correlation between the input constraint and the performance constraint, a novel prescribed performance control (PPC) approach is proposed. Compared with the existing \"horn\" shape performance boundary-based PPC methods, the limitation of having to repeatedly debug design parameters or sacrifice initial transient performance to meet different initial error requirements is eliminated. Meanwhile, the coupling effect between the input constraint and the performance constraint is also considered, and the balance between input safety and control performance is achieved by constructing an auxiliary system. Furthermore, combining identifier-critic-actor structure-based RL strategy and backstepping technique, a sliding flexible PPB-guided reinforcement learning (SFPPB-RL) optimal control algorithm is developed, which minimizes the cost function while ensuring input safety and prescribed performance indicators. The validity of the proposed algorithm is demonstrated via simulations.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"14 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140208","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

Practically Time-Synchronized Command Filtered Backstepping Control of Nonlinear Systems. 非线性系统的实际时间同步命令滤波反步控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-25 DOI: 10.1109/tcyb.2025.3610312

Guozeng Cui,Hui Xu,Juping Gu,Shengyuan Xu

{"title":"Practically Time-Synchronized Command Filtered Backstepping Control of Nonlinear Systems.","authors":"Guozeng Cui,Hui Xu,Juping Gu,Shengyuan Xu","doi":"10.1109/tcyb.2025.3610312","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3610312","url":null,"abstract":"This article concentrates on the problem of practically time-synchronized tracking control for multi-input multi-output (MIMO) systems with unmatched nonlinearities and input saturations. Different from the existing approaches, a practically time-synchronized command filtered backstepping (CFB) control scheme is proposed. By integrating modified command filters and control signals designed with norm-normalized sign functions, the newly developed framework not only retains the advantages of the CFB control approach but also guarantees the property of time-synchronized convergence. Specifically, the \"explosion of complexity\" phenomenon and the influence of filtering errors are simultaneously addressed, and all components of the tracking error can achieve practically synchronous convergence to a small neighborhood of the origin in a finite time, despite the presence of unmatched nonlinearities in high-order systems. Furthermore, novel auxiliary systems are recursively embedded into each step of the time-synchronized CFB design to counteract the effect of input saturation. Rigorous theoretical analyses and comparative simulations demonstrate the rationality, effectiveness, and superiority of the proposed control scheme.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"86 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140099","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

Evolution of Transferable and Self-Organized Communication Modules for Solving Multiple Swarm Robotics Tasks. 求解多群机器人任务的可转移自组织通信模块的演化。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-25 DOI: 10.1109/tcyb.2025.3610013

Rafael Sendra-Arranz,Alvaro Gutierrez,Anders Lyhne Christensen

{"title":"Evolution of Transferable and Self-Organized Communication Modules for Solving Multiple Swarm Robotics Tasks.","authors":"Rafael Sendra-Arranz,Alvaro Gutierrez,Anders Lyhne Christensen","doi":"10.1109/tcyb.2025.3610013","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3610013","url":null,"abstract":"A key aspect of decentralized multirobot coordination is communication. However, beyond simple signaling, there are only few reports in the literature on the successful evolution of communication, with successes largely dependent on specific tasks and evolutionary setups. Thus, there is a lack of standardized communication frameworks that can be applied to different tasks without the need to redesign, rebuild, or re-evolve the entire system for every new task. In this article, we propose a novel communication module that does not need to be modified for its use in different tasks. Each robot has a coordinate (state) in a virtual communication space. The communication space is partitioned into virtual regions, and each region is linked to a physical behavior, such as seeking resources, phototaxis, or recharging the battery. A robot's individual behavior is determined by the region to which its current communication state belongs. Since robots can navigate the communication space and continually broadcast their coordinates to neighbors within range, robot swarms can effectively coordinate their behavior in a self-organized manner. We demonstrate that the same evolved communication module is effective in three swarm robotics tasks: 1) the physical aggregation of the robots into groups of a desired size; 2) the formation of desired swarm geometries; and 3) a foraging task based on temporal role allocation. The results show that the communication module provides good and scalable performance in all tasks, representing a significant step toward a task-agnostic communication framework for robot swarms.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"95 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140220","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

Time-Frequency Collaborative Learning for Imbalanced Ship Motion Data With Missing Labels in Sea State Estimation. 海况估计中缺失标签不平衡船舶运动数据的时频协同学习。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-25 DOI: 10.1109/tcyb.2025.3610416

Shuxin Li,Mengna Liu,Xu Cheng,Junhao Xiao,Shengyong Chen

{"title":"Time-Frequency Collaborative Learning for Imbalanced Ship Motion Data With Missing Labels in Sea State Estimation.","authors":"Shuxin Li,Mengna Liu,Xu Cheng,Junhao Xiao,Shengyong Chen","doi":"10.1109/tcyb.2025.3610416","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3610416","url":null,"abstract":"Semi-supervised learning (SSL) has gained significant attention in the domain of sea state estimation (SSE) due to its capacity to alleviate the reliance of deep learning models on extensive labeled datasets. While existing semi-supervised SSE methodologies leveraging pseudo-labeling have achieved promising results, they often overlook the challenges posed by high class imbalance and the prevalence of missing data in ship motion datasets, which restricts their broader applicability. In this article, we propose a novel SSL approach BalanceSSE based on the class-imbalanced ship motion data for SSE. This approach consists of three main modules: 1) the dynamic imputation (DIT); 2) the imbalance temporal-frequency learning (ITFL); and 3) the ClusterProx classifier (CL). The DIT module dynamically imputes incomplete ship motion data by assigning different weights to various dimensions data. The ITFL module employs time-frequency collaborative learning to generate pseudo-labels and integrate an adaptive confidence strategy to select high confidence pseudo-labels. This process is further enhanced by the CL module to produce better estimates. Experimental tests on UCR datasets and ship motion datasets demonstrate that BalanceSSE outperforms state-of-the-art methods. Ablation studies highlight the critical role of each module in BalanceSSE.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"41 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145140101","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

ε-Dependent/Independent Dynamic Event-Triggered Control of Switched Two-Time-Scale Systems 切换双时间尺度系统的ε依赖/独立动态事件触发控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-24 DOI: 10.1109/tcyb.2025.3610484

Ze-Hong Zeng, Yan-Wu Wang, Xiao-Kang Liu, Jiang-Wen Xiao

引用次数: 0

Composite Anti-Disturbance Control for Networked Systems With Disturbances and Actuator Attacks via Event-Triggered Output Feedback 具有干扰和执行器攻击的网络系统的事件触发输出反馈复合抗干扰控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-09-24 DOI: 10.1109/tcyb.2025.3609819

Ning Zhao, Di Lun, Huiyan Zhang, Xudong Zhao, Imre J. Rudas

引用次数: 0