IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第9页

Distributed Aggregative Optimization of MASs Subject to Coupled Inequality Constraints 耦合不等式约束下质量的分布式聚合优化

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-03-10 DOI: 10.1109/TSMC.2025.3624247

Rong Nie;Wenli Du;Zhongmei Li;Shuping He

{"title":"Distributed Aggregative Optimization of MASs Subject to Coupled Inequality Constraints","authors":"Rong Nie;Wenli Du;Zhongmei Li;Shuping He","doi":"10.1109/TSMC.2025.3624247","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3624247","url":null,"abstract":"This article investigates the distributed aggregation optimization problem in multiagent systems (MASs), with a particular focus on addressing the aggregation effect commonly encountered in modern engineering and technological applications. In such scenarios, the local objective function of an agent depends not only on its own decision variables but also interacts with the decision variables of other agents, resulting in complex coupling relationships. To solve these challenges while ensuring that the optimization variables satisfy the coupled inequality constraints, this article introduces a novel framework called distributed aggregative parameter projection (DAPP). Specifically, the proposed distributed protocol is based on an improved parameter projection, including two direction updates, which minimizes the cost function and keeps the search direction obeying the inequality at each iteration. In addition, the linear convergence performance of the proposed scheme over the undirected and connected graph is ensured by rigorous theoretical proof with mild assumptions. Finally, simulation results demonstrate the superior performance of DAPP with smaller global function and faster convergence speed in comparison to the existing method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2253-2261"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504423","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

IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information IEEE系统、人与控制论汇刊：系统出版信息

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-03-18 DOI: 10.1109/TSMC.2026.3669272

引用次数: 0

Fixed-Size Dynamic Scale-Free Networks: Modeling, Stationarity, and Resilience 固定大小的动态无标度网络：建模、平稳性和弹性

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-12 DOI: 10.1109/TSMC.2025.3649867

Yichao Yao;Minyu Feng;Matjaž Perc;Jürgen Kurths

{"title":"Fixed-Size Dynamic Scale-Free Networks: Modeling, Stationarity, and Resilience","authors":"Yichao Yao;Minyu Feng;Matjaž Perc;Jürgen Kurths","doi":"10.1109/TSMC.2025.3649867","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3649867","url":null,"abstract":"Many real-world scale-free networks, such as neural networks and online communication networks, consist of a fixed number of nodes but exhibit dynamic edge fluctuations. However, traditional models frequently overlook scenarios where the node count remains constant, instead prioritizing node growth. In this work, we depart from the assumptions of node number variation and preferential attachment to present an innovative model that conceptualizes node degree fluctuations as a state-dependent random walk process with stasis and variable diffusion coefficient. We show that this model yields stochastic dynamic networks with stable scale-free properties. Through comprehensive theoretical and numerical analyses, we demonstrate that the degree distribution converges to a power-law distribution, provided that the lowest degree state within the network is not an absorbing state. Furthermore, we investigate the resilience of the fraction of the largest component and the average shortest path length following deliberate attacks on the network. By using three real-world networks, we confirm that the proposed model accurately replicates actual data. The proposed model thus elucidates mechanisms by which networks, devoid of growth and preferential attachment features, can still exhibit power-law distributions and be used to simulate and study the resilience of attacked fixed-size scale-free networks.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2570-2582"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504738","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

A Novel Compound Fault Diagnosis Method by Combining Feature Conditional Diffusion and Capsule Networks Under Scarce Samples 基于特征条件扩散和胶囊网络的稀有样本复合故障诊断方法

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1109/TSMC.2026.3652132

Xin-Zhe Su;Huimin Wang;Guang-Hong Yang

引用次数: 0

Prescribed-Time Containment Control for Multiple Mobile Robot Systems 多移动机器人系统的规定时间控制

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1109/TSMC.2025.3650119

Jiaming Zhang;Yang Liu;Ben Niu;Wenling Li;Bin Zhang

{"title":"Prescribed-Time Containment Control for Multiple Mobile Robot Systems","authors":"Jiaming Zhang;Yang Liu;Ben Niu;Wenling Li;Bin Zhang","doi":"10.1109/TSMC.2025.3650119","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3650119","url":null,"abstract":"This article investigates the prescribed-time containment control problem for multiple wheeled mobile robots (MWMRs) with unknown control gains. A novel and generalized prescribed-time stability theorem is established, significantly advancing existing frameworks and facilitating controller design. For each follower, a distributed containment observer is developed to ensure that the observer state converges precisely to the convex hull formed by multiple leaders within a prescribed time. This approach effectively transforms the containment control problem into a more tractable tracking control problem, meanwhile enabling local reconstruction of unmeasured neighbor states using available observer outputs. Then, the type-B Nussbaum function is employed to eliminate the effects of the time-varying unknown control coefficient. Based on the developed prescribed-time theorem and the time-varying parametric Lyapunov equation (PLE), the fully actuated controllers are designed for the MWMRs. It can guarantee that the containment errors converge to zero within the prescribed time. Finally, simulation results validate the effectiveness of the proposed method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2388-2398"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506200","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

Vibration Representation and Speed-Joint Network for Machinery Fault Diagnosis Under Time-Varying Conditions With Sparse Fault Data 基于稀疏故障数据时变条件下机械故障诊断的振动表示与速度联合网络

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-21 DOI: 10.1109/TSMC.2026.3651692

Wenyi Liu;Chaoang Xiao;Jianbo Yu

{"title":"Vibration Representation and Speed-Joint Network for Machinery Fault Diagnosis Under Time-Varying Conditions With Sparse Fault Data","authors":"Wenyi Liu;Chaoang Xiao;Jianbo Yu","doi":"10.1109/TSMC.2026.3651692","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3651692","url":null,"abstract":"Machinery fault diagnosis with limited fault data under time-varying conditions remains a persistent challenge. In practical industrial settings, healthy samples are generally abundant during daily monitoring, while fault data is sparse. This hinders further development of dynamic fault recognition technology. In this article, a novel self-supervised vibration representation and speed-joint network, i.e., VRSNet, is proposed to conduct few-shot fault diagnosis under time-varying speed conditions. First, contrastive learning (CL) is employed to achieve feature self-representation using only healthy vibration signals. This enhances the discriminability of extractor and facilitates the self-extraction of feature manifolds across varying speeds. Second, a dimension-lifting network named approximator (APP) is proposed to identify the potential relationship between rotational speeds and corresponding vibration representations, which facilitates condition alignment for each transient speed and cross-modal feature fusion. Finally, a vibration-speed-joint fine-tuning method is proposed to implement few-shot training for fault recognition, where the norm-freezing strategy effectively preserves the pretraining knowledge of both vibration and speed branches for generalization enhancement. VRSNet is verified through two cases under time-varying speed conditions. The results demonstrate that it outperforms those state-of-the-art (SOTA) methods and achieves a recognition accuracy of 84.75% in a 1-shot fault data scenario with varying speeds. Even when confronted with faults at previously unseen speeds, VRSNet can still recognize them accurately.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2608-2621"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506544","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 Model Ensemble Adaptive Trust Region Policy Optimization 多样性驱动模型集成自适应信任域策略优化

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1109/TSMC.2026.3650850

Haotian Xu;Zheng Yan;Junyu Xuan;Guangquan Zhang;Jie Lu

{"title":"Diversity-Driven Model Ensemble Adaptive Trust Region Policy Optimization","authors":"Haotian Xu;Zheng Yan;Junyu Xuan;Guangquan Zhang;Jie Lu","doi":"10.1109/TSMC.2026.3650850","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3650850","url":null,"abstract":"Model-based reinforcement learning (MBRL) aims to promote sample efficiency and reduce the number of interactions with the true environment, via learning an environment dynamic model, compared with model-free reinforcement learning (MFRL). However, the success of MBRL heavily relies on two key aspects: model learning and planning. The former refers to learning an accurate model, and the latter aims to improve the behavior policy. In this article, we investigate these two aspects further with model ensemble learning. We design a deep residual attention U-Net (RauNet) with fewer neurons (or weights) than the widely used shallow neural network as our base models and further apply the Hilbert–Schmidt independence criterion (HSIC) as a regularization term to pursue model diversity explicitly for the model ensemble. Furthermore, we propose an adaptive trust region policy optimization (TRPO), in which the parametric Rényi alpha divergence substitutes for the Kullback–Leibler (KL) divergence for measuring the difference between two successive policies, and the alpha value can be adaptively adjusted during TRPO training iterations. This method is called diversity-driven model ensemble adaptive TRPO, or simply diversity-driven model ensemble adaptive trust region policy optimization. Our detailed experiments on six benchmark environments show that our proposed approach is optimal, compared with five state-of-the-art RL techniques.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2622-2635"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506554","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

A Distributionally Robust Optimization-Based Stochastic Self-Triggered Model Predictive Control 基于分布鲁棒优化的随机自触发模型预测控制

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-03-05 DOI: 10.1109/TSMC.2025.3649661

Bin Li;Wenhao Zhang;Mingming Shi

引用次数: 0

Separation Approach to Adaptive Control for Robot Manipulators Without Joint-Space and Task-Space Velocity Measurements 无关节空间和任务空间速度测量的机械臂自适应控制分离方法

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-01-14 DOI: 10.1109/TSMC.2025.3649270

Bo Zhang;Changchun Hua;Jiafeng Zhou;Jian Zhang;Yu Zhang

引用次数: 0

Multidomain Evolutionary Optimization on Adversarial Link Perturbation in Imbalanced-Size Complex Systems 非平衡规模复杂系统中对抗环节扰动的多域进化优化

IF 8.4 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2026-04-01 Epub Date: 2026-02-24 DOI: 10.1109/TSMC.2026.3652853

Jie Zhao;Kang Hao Cheong

{"title":"Multidomain Evolutionary Optimization on Adversarial Link Perturbation in Imbalanced-Size Complex Systems","authors":"Jie Zhao;Kang Hao Cheong","doi":"10.1109/TSMC.2026.3652853","DOIUrl":"https://doi.org/10.1109/TSMC.2026.3652853","url":null,"abstract":"Real-world complex systems usually share structural characteristics such as the small-world property, power-law distributions, and community structure. Multidomain evolutionary optimization (MDEO) leverages these commonalities to search for optimal solutions in multiple domains simultaneously. However, it still faces challenges in achieving effective cooperation when the networks involved are of highly imbalanced sizes. To address this issue, we propose the harmonized MDEO (HMDEO), in which two graph coarsening strategies are developed to jointly coarsen the large network to a fine level, acting as the bridge between the large network and the small network for knowledge exchange. Following that, we propose a bidirectional optimization framework incorporating two cases: large-to-small and small-to-large, allowing solutions optimized in one domain to be seamlessly transferred to another domain of varied scales. To enhance applicability, we also tailor the measurement of network similarity and the network alignment strategy targeted to imbalanced-size scenarios. The effectiveness of HMDEO is demonstrated through experiments on several pairs of networks of differing scales, where HMDEO outperforms other optimization approaches in addressing adversarial edge perturbation against community detection in complex systems.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"56 4","pages":"2708-2719"},"PeriodicalIF":8.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507311","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