IEEE Transactions on Cybernetics最新文献_第3页

Social Informer: Pedestrian Trajectory Prediction by Informer With Adaptive Trajectory Probability Region Optimization 社会信息器：基于自适应轨迹概率区域优化的信息器预测行人轨迹

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-10-06 DOI: 10.1109/tcyb.2025.3613498

Zihan Jiang, Rui Yang, Yiqun Ma, Chengxuan Qin, Xiaohan Chen, Zidong Wang

引用次数: 0

Multidomain Evolutionary Optimization on Combinatorial Problems in Complex Networks 复杂网络组合问题的多域进化优化

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-10-06 DOI: 10.1109/tcyb.2025.3592664

Jie Zhao, Kang Hao Cheong, Yaochu Jin

引用次数: 0

Dynamic Event-Triggered Model Reference Adaptive Control for Uncertain Switched Systems 不确定切换系统的动态事件触发模型参考自适应控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-10-03 DOI: 10.1109/tcyb.2025.3612045

Dong Yang, Qi Zhang, Guangdeng Zong, Haibin Sun, Ying Zhao

引用次数: 0

History-Guided Prompt Generation for Vision-and-Language Navigation. 用于视觉和语言导航的历史引导提示生成。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-10-02 DOI: 10.1109/tcyb.2025.3613147

Wen Guo,Zongmeng Wang,Yufan Hu,Junyu Gao

{"title":"History-Guided Prompt Generation for Vision-and-Language Navigation.","authors":"Wen Guo,Zongmeng Wang,Yufan Hu,Junyu Gao","doi":"10.1109/tcyb.2025.3613147","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3613147","url":null,"abstract":"Vision-and-language navigation (VLN) has garnered extensive attention in the field of embodied artificial intelligence. VLN involves time series information, where historical observations contain rich contextual knowledge and play a crucial role in navigation. However, current methods do not explicitly excavate the connection between rich contextual information in history and the current environment, and ignore adaptive learning of clues related to the current environment. Therefore, we explore a Prompt Learning-based strategy which adaptively mines information in history that is highly relevant to the current environment to enhance the agent's perception of the current environment and propose a history-guided prompt generation (HGPG) framework. Specifically, HGPG includes two parts, one is an entropy-based history acquisition module that assesses the uncertainty of the action probability distribution from the preceding step to determine whether historical information should be used at the current time step. The other part is the prompt generation module that transforms historical context into prompt vectors by sampling from an end-to-end learned token library. These prompt tokens serve as discrete, knowledge-rich representations that encode semantic cues from historical observations in a compact form, making them easier for the decision network to understand and utilize. In addition, we share the token library across various navigation tasks, mining common features between different tasks to improve generalization to unknown environments. Extensive experimental results on four mainstream VLN benchmarks (R2R, REVERIE, SOON, R2R-CE) demonstrate the effectiveness of our proposed method. Code is available at https://github.com/Wzmshdong/HGPG.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"10 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209329","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

Distributed Time-Varying Formation Control With Obstacle Avoidance of Multiagent Systems Under Switching Topologies. 切换拓扑下多智能体系统的分布式时变避障编队控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-10-02 DOI: 10.1109/tcyb.2025.3613860

Yi Su,Yaping Sun,Xinsong Yang,Wenwu Yu,Xiaochuan Yang

{"title":"Distributed Time-Varying Formation Control With Obstacle Avoidance of Multiagent Systems Under Switching Topologies.","authors":"Yi Su,Yaping Sun,Xinsong Yang,Wenwu Yu,Xiaochuan Yang","doi":"10.1109/tcyb.2025.3613860","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3613860","url":null,"abstract":"Distributed formation tracking control with obstacle avoidance of multiagent systems (MASs) under random switching topologies and external disturbances is considered in this article. To achieve the complex objective, an effective control strategy is developed in three steps. First, under the transition probability (TP)-based mode-dependent average dwell-time (MDADT) switching topologies, a distributed objective trajectory achieves almost sure global exponential tracking of the desired formation trajectory. Second, a safe objective trajectory approach is designed by geometrically projecting the unsafe parts of the existing formation trajectory onto the boundary of the obstacle region. Finally, an integral-multiplicative barrier Lyapunov function (IMBLF) is proposed to allow agents to track the safe objective trajectory, where the IMBLF can further guarantee the safety of the MASs. One of the interesting merits of our results is that the impulsive increasing of Lyapunov function at switching instants which is necessary for classical analysis methods has been removed. The feasibility of the proposed formation control method with obstacle avoidance is verified by simulations.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"2 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209332","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

Consensus Analysis and Convergence Rate Optimization for Open Multiagent Systems. 开放多智能体系统的一致性分析与收敛速度优化。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-10-02 DOI: 10.1109/tcyb.2025.3613690

Zhian Jia,Ming Chi,Zhi-Wei Liu,Jing-Zhe Xu

引用次数: 0

Achieving Convex Optimization Within Prescribed Time for Networked Euler-Lagrange Systems: A Novel Adaptive Distributed Approach With Small-Gain Conditions. 网络欧拉-拉格朗日系统在规定时间内实现凸优化：一种具有小增益条件的自适应分布式方法。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-10-02 DOI: 10.1109/tcyb.2025.3611131

Gewei Zuo,Mengmou Li,Yujuan Wang,Lijun Zhu,Yongduan Song

{"title":"Achieving Convex Optimization Within Prescribed Time for Networked Euler-Lagrange Systems: A Novel Adaptive Distributed Approach With Small-Gain Conditions.","authors":"Gewei Zuo,Mengmou Li,Yujuan Wang,Lijun Zhu,Yongduan Song","doi":"10.1109/tcyb.2025.3611131","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3611131","url":null,"abstract":"In this article, we address the problem of prescribed-time distributed convex optimization (DCO) for a class of networked Euler-Lagrange systems (NELSs) operating over undirected connected graphs. By utilizing position-dependent measured gradient values of local objective functions and facilitating local information exchanges among neighboring agents, we construct a set of auxiliary systems that collaboratively seek the optimal solution. The prescribed-time DCO problem is then reformulated as a prescribed-time stabilization challenge of an interconnected error system. We propose a prescribed-time small-gain criterion to characterize the prescribed-time stabilization of the system, presenting a novel approach that enhances effectiveness beyond existing asymptotic or finite-time stabilization methods for interconnected systems. Based on this criterion and the auxiliary systems, we design innovative adaptive prescribed-time local tracking controllers for the subsystems. The prescribed-time convergence is achieved through the introduction of time-varying gains that increase to infinity as time approaches the prescribed deadline. The Lyapunov function, along with prescribed-time mapping, is employed to establish the prescribed-time stability of the closed-loop system and the boundedness of internal signals. Finally, the theoretical results are validated through a numerical example.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"23 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145209331","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

Predictor-Based Fractional-Order Sliding Mode LFC for Interconnected Power Systems With Input Delay. 基于预测器的输入延迟互联电力系统分数阶滑模LFC。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-10-02 DOI: 10.1109/tcyb.2025.3605888

Xue Yu,Gang Wang,Yuan Zhong,Huaguang Zhang,Jinhai Liu

引用次数: 0

ARF: Arbitrary Routing Framework for All-in-One Image Restoration ARF：用于一体化图像恢复的任意路由框架

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-10-01 DOI: 10.1109/tcyb.2025.3604401

Yimin Xu, Nanxi Gao, Yunshan Zhong, Fei Chao, Rongrong Ji

引用次数: 0

Output Feedback Control for Fuzzy Singularly Perturbed Systems Under Nonuniform Sampling 非均匀采样下模糊奇异摄动系统的输出反馈控制