IEEE Transactions on Cybernetics最新文献_第4页

Hierarchical Containment Control With Bipartite Cluster Consensus for Heterogeneous Multiagent Systems Under Layer-Signed Digraph 层签名有向图下异构多智能体系统的二部聚类一致性层次包容控制

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-10 DOI: 10.1109/TCYB.2024.3506986

Dazhong Ma;Jingshu Sang;Lei Liu;Zhanshan Wang

{"title":"Hierarchical Containment Control With Bipartite Cluster Consensus for Heterogeneous Multiagent Systems Under Layer-Signed Digraph","authors":"Dazhong Ma;Jingshu Sang;Lei Liu;Zhanshan Wang","doi":"10.1109/TCYB.2024.3506986","DOIUrl":"10.1109/TCYB.2024.3506986","url":null,"abstract":"This article considers the hierarchical containment control (HCC) for flexible mirrored collaboration, which accommodates the bipartite cluster consensus behavior in two symmetric convex hulls formed by multiple leaders. First, to achieve the mirrored collaboration in symmetric convex hulls, the layer-signed digraph is generated by involving the antagonistic interaction. Benefiting from the hierarchical structure, the antagonistic interaction in the assistant-layer replaces the assumption of in-degree balance for the existing cluster consensus issues. Second, the existing types of control protocols and the framework of cooperative output regulation limit the achievement of the studied hierarchical mirrored collaboration. To solve this problem, the hierarchical cooperative output regulation is extended based on the formulated hierarchical mirrored collaborative errors. Third, the layer-signal compensator is designed estimating the states of leaders as well as guaranteeing the convergence of collaborative behaviors. Combining with the designed layer-signal compensator, a novel HCC protocol is proposed so that the bipartite cluster consensus behavior can be achieved simultaneously in two symmetric convex hulls. Finally, theoretical results are verified by performing the numerical simulation.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"765-775"},"PeriodicalIF":9.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961511","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

Adaptive Event-Triggered Lag Outer Synchronization for Coupled Neural Networks With Multistate or Multiderivative Couplings 多状态或多导数耦合神经网络的自适应事件触发滞后外同步

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-10 DOI: 10.1109/tcyb.2024.3519171

Jin-Liang Wang, Yan-Ran Zhu, Jian-Qiao Wang, Shun-Yan Ren, Tingwen Huang

引用次数: 0

FingHV: Efficient Sharing and Fine-Grained Scheduling of Virtualized HPU Resources FingHV：虚拟化HPU资源的高效共享和细粒度调度

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-10 DOI: 10.1109/TCYB.2024.3518569

Hui Wang;Zhiwen Yu;Zhuoli Ren;Yao Zhang;Jiaqi Liu;Liang Wang;Bin Guo

{"title":"FingHV: Efficient Sharing and Fine-Grained Scheduling of Virtualized HPU Resources","authors":"Hui Wang;Zhiwen Yu;Zhuoli Ren;Yao Zhang;Jiaqi Liu;Liang Wang;Bin Guo","doi":"10.1109/TCYB.2024.3518569","DOIUrl":"10.1109/TCYB.2024.3518569","url":null,"abstract":"While artificial intelligence (AI) technology has advanced in real-world applications, there is a strong motivation to develop hybrid systems where AI algorithms and humans collaborate, promoting more human-centered approaches in AI system design. This has led to the emergence of a novel human-machine computing (HMC) paradigm, which combines human cognitive abilities with machine computational power to create a collaborative computing framework that meets the demands of large-scale, complex tasks and enables human-machine symbiosis. Human processing units (HPUs) are crucial computing resources in HMC-oriented systems, and efficient HPU resource provisioning is key to boosting system performance. However, existing schemes often fail to assign tasks to the most suitable HPUs and optimize HPU utility, as they either cannot quantitatively measure skills or overlook utility concerns during task assignment and scheduling. To address these challenges, this article proposes a fine-grained HPU virtualization (FingHV) approach, which leverages virtualization techniques to improve flexibility, fairness, and utility in the provisioning process. The core idea is to use a tree-based skill model to precisely measure the levels and correlations of multiple skills within individual HPUs, and to apply a mixed time/event-based scheduling policy to maximize HPU utility. Specifically, we begin by proposing a hierarchical multiskill tree to model HPU skills and their correlations. Next, we formulate the HPU virtualization problem and present a fine-grained virtualization method, which includes a quality-driven HPU assignment process and a mixed time/event-based scheduling policy to improve resource-sharing efficiency. Finally, we evaluate FingHV on a synthetic dataset with varying task sizes and a real-world case. The results demonstrate that FingHV improves global matching quality by up to 39.7% and increases HPU utility by 11.2% compared to the baselines.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"600-614"},"PeriodicalIF":9.4,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961510","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

Auxiliary Task-Based Deep Reinforcement Learning for Quantum Control 量子控制中基于辅助任务的深度强化学习

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-07 DOI: 10.1109/TCYB.2024.3521300

Shumin Zhou;Hailan Ma;Sen Kuang;Daoyi Dong

{"title":"Auxiliary Task-Based Deep Reinforcement Learning for Quantum Control","authors":"Shumin Zhou;Hailan Ma;Sen Kuang;Daoyi Dong","doi":"10.1109/TCYB.2024.3521300","DOIUrl":"10.1109/TCYB.2024.3521300","url":null,"abstract":"Due to its property of not requiring prior knowledge of the environment, reinforcement learning (RL) has significant potential for solving quantum control problems. In this work, we investigate the effectiveness of continuous control policies based on deep deterministic policy gradient. To achieve good control of quantum systems with high fidelity, we propose an auxiliary task-based deep RL (AT-DRL) for quantum control. In particular, we design an auxiliary task to predict the fidelity value, sharing partial parameters with the main network (from the main RL task). The auxiliary task learns synchronously with the main task, allowing one to extract intrinsic features of the environment, thus aiding the agent to achieve the desired state with high fidelity. To further enhance the control performance, we also design a guided reward function based on the fidelity of quantum states that enables gradual fidelity improvement. Numerical simulations demonstrate that the proposed AT-DRL can provide a good solution to the exploration of quantum dynamics. It not only achieves high task fidelities but also demonstrates fast learning rates. Moreover, AT-DRL has great potential in designing control pulses that achieve effective quantum state preparation.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"712-725"},"PeriodicalIF":9.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936235","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

Target–Attackers–Defenders Linear–Quadratic Exponential Stochastic Differential Games With Distributed Control 目标-攻击者-防御者分布控制线性-二次指数随机微分对策

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-06 DOI: 10.1109/TCYB.2024.3508694

Guilu Li;Jianan Wang;Fuxiang Liu;Fang Deng

{"title":"Target–Attackers–Defenders Linear–Quadratic Exponential Stochastic Differential Games With Distributed Control","authors":"Guilu Li;Jianan Wang;Fuxiang Liu;Fang Deng","doi":"10.1109/TCYB.2024.3508694","DOIUrl":"10.1109/TCYB.2024.3508694","url":null,"abstract":"This article investigates stochastic differential games involving multiple attackers, defenders, and a single target, with their interactions defined by a distributed topology. By leveraging principles of topological graph theory, a distributed design strategy is developed that operates without requiring global information, thereby minimizing system coupling. Additionally, this study extends the analysis to incorporate stochastic elements into the target-attackers–defenders games, moving beyond the scope of deterministic differential games. Using the direct method of completing the square and the Radon-Nikodym derivative, we derive optimal distributed control strategies for two scenarios: one where the target follows a predefined trajectory and another where it has free maneuverability. In both scenarios, our research demonstrates the effectiveness of the designed control strategies in driving the system toward a Nash equilibrium. Notably, our algorithm eliminates the need to solve the coupled Hamilton-Jacobi equation, significantly reducing computational complexity. To validate the effectiveness of the proposed control strategies, numerical simulations are presented in this article.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"574-587"},"PeriodicalIF":9.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934510","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

Payoff Control in Multichannel Games: Influencing Opponent Learning Evolution 多渠道博弈中的收益控制：影响对手学习进化

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-03 DOI: 10.1109/TCYB.2024.3507830

Juan Shi;Chen Chu;Guoxi Fan;Die Hu;Jinzhuo Liu;Zhen Wang;Shuyue Hu

引用次数: 0

Distributed Resilient Energy Management for Seaport Microgrid Against Stealthy Attacks With Limited Security Defense Resource 港口微电网有限安全防御资源下的分布式弹性能源管理

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-03 DOI: 10.1109/TCYB.2024.3514693

Fei Teng;Xin Zhang;Tieshan Li;Qihe Shan;C. L. Philip Chen;Yushuai Li

{"title":"Distributed Resilient Energy Management for Seaport Microgrid Against Stealthy Attacks With Limited Security Defense Resource","authors":"Fei Teng;Xin Zhang;Tieshan Li;Qihe Shan;C. L. Philip Chen;Yushuai Li","doi":"10.1109/TCYB.2024.3514693","DOIUrl":"10.1109/TCYB.2024.3514693","url":null,"abstract":"This article investigates the distributed resilient energy management (EM) strategy for the seaport microgrid under stealthy attacks. First, based on an analysis of seaport microgrid characteristics, we construct an EM model that aims at minimizing both operating cost and security defense resource (SDR) cost. Second, we present a distributed, resilient strategy by defining node security levels and establishing dynamic security intervals. We prove that the gap between the obtained feasible solution and the optimal one is bounded. The designed strategy is capable of tolerating the effect of the unlimited number of stealthy attacked nodes on the seaport microgrid. In addition, given the limited SDRs for the resilient EM of the island seaport microgrid, a distributed mechanism for searching the minimum security connected dominating set (MSCDS) is proposed to minimize the size of trusted nodes. Finally, simulation results demonstrate the effectiveness of the proposed strategy. Note to Practitioners: This article addresses the vulnerability of the seaport microgrid, a critical issue that impacts EM and disrupts seaport operations. Current approaches to seaport EM do not account for potential attacks. Meanwhile, existing methods for attack resilience often overlook the costs of security resources. We propose a new approach for the distributed and resilient EM of the island seaport microgrid. Secure operation is achieved by protecting the fewest trusted nodes, thereby conserving SDRs. We then show how this algorithm (searching the MSCDS) can be efficiently designed. Preliminary simulations indicate its feasibility, though it has yet to be tested in a production environment. Future research will focus on designing trusted nodes within dynamic topologies.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"917-926"},"PeriodicalIF":9.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924588","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

ADG-Net: A Sim2Real Multimodal Learning Framework for Adaptive Dexterous Grasping ADG-Net：一种用于自适应灵巧抓取的Sim2Real多模态学习框架

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-03 DOI: 10.1109/TCYB.2024.3518975

Hui Zhang;Jianzhi Lyu;Chuangchuang Zhou;Hongzhuo Liang;Yuyang Tu;Fuchun Sun;Jianwei Zhang

{"title":"ADG-Net: A Sim2Real Multimodal Learning Framework for Adaptive Dexterous Grasping","authors":"Hui Zhang;Jianzhi Lyu;Chuangchuang Zhou;Hongzhuo Liang;Yuyang Tu;Fuchun Sun;Jianwei Zhang","doi":"10.1109/TCYB.2024.3518975","DOIUrl":"10.1109/TCYB.2024.3518975","url":null,"abstract":"In this article, a novel simulation-to-real (sim2real) multimodal learning framework is proposed for adaptive dexterous grasping and grasp status prediction. A two-stage approach is built upon the Isaac Gym and several proposed pluggable modules, which can effectively simulate dexterous grasps with multimodal sensing data, including RGB-D images of grasping scenarios, joint angles, 3-D tactile forces of soft fingertips, etc. Over 500K multimodal synthetic grasping scenarios are collected for neural network training. An adaptive dexterous grasping neural network (ADG-Net) is trained to learn dexterous grasp principles and predict grasp parameters, employing an attention mechanism and a graph convolutional neural network module to fuse multimodal information. The proposed adaptive dexterous grasping method can detect feasible grasp parameters from an RGB-D image of a grasp scene and then optimize grasp parameters based on multimodal sensing data when the dexterous hand touches a target object. Various experiments in both simulation and physical grasps indicate that our ADG-Net grasping method outperforms state-of-the-art grasping methods, achieving an average success rate of 92% for grasping isolated unseen objects and 83% for stacked objects. Code and video demos are available at <uri>https://github.com/huikul/adgnet</uri>.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"840-853"},"PeriodicalIF":9.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924589","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

Adaptive Descriptor Sliding-Mode Observer-Based Dynamic Event-Triggered Consensus of Multiagent Systems Against Actuator and Sensor Faults 基于自适应广义滑模观测器的多智能体系统在执行器和传感器故障情况下的动态事件触发一致性

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-03 DOI: 10.1109/TCYB.2024.3519593

Zhengyu Ye;Bin Jiang;Ziquan Yu;Yuehua Cheng

引用次数: 0

Global Dynamic Double Side Event-Triggered Adaptive Control for Interconnected Nonlinear Systems via Intermittent Output Feedback 基于间歇输出反馈的互联非线性系统全局动态双侧事件触发自适应控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-01-01 DOI: 10.1109/tcyb.2024.3518773

Hao Li, Changchun Hua, Kuo Li

引用次数: 0