IEEE Transactions on Cybernetics最新文献

A New Arbitrary-Time Guaranteed-Performance Adaptive Tracking Control Scheme Design for Uncertain Nonlinear Systems. 一种新的不确定非线性系统任意时间保证性能自适应跟踪控制方案设计。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-08 DOI: 10.1109/TCYB.2026.3689094

Huixin Jiang, Yana Yang, Changchun Hua, Junpeng Li

{"title":"A New Arbitrary-Time Guaranteed-Performance Adaptive Tracking Control Scheme Design for Uncertain Nonlinear Systems.","authors":"Huixin Jiang, Yana Yang, Changchun Hua, Junpeng Li","doi":"10.1109/TCYB.2026.3689094","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3689094","url":null,"abstract":"The arbitrary-time (AT) nonovershoot adaptive tracking control issue is investigated for uncertain high-order nonlinear systems with guaranteed-performance (GP). Superior to the existing prescribed-time prescribed-performance control strategies that are vulnerable to suddenly unexpected external disturbances, a novel globally segmented AT guaranteed-performance (ATGP) function is proposed to overcome such limitations. First, the tracking errors of the system are driven to zero not only within the GP constraints but within an AT, which greatly enhances the execution efficiency. Then, a robust error-induced time-triggered disturbance-rejection mechanism is proposed to eliminate the singularity issue in conventional prescribed-performance control and enable a stabilized system to regain ATGP convergence under unexpected disturbances. Furthermore, compared with traditional adaptive algorithms that only achieve the ultimately uniform boundedness, an AT parameter adaptive law is proposed, driving the adaptive estimation error to zero. Finally, a practical example on a manipulator and a numerical example on a general nonlinear system are taken to authentically corroborate the efficacy of the proposed control algorithm.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856269","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

Dynamic Gain-Driven Adaptive Quantized Output Feedback Control for Nonlinear Systems Governed by Parameter Criteria. 基于参数准则的非线性系统动态增益驱动自适应量化输出反馈控制。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-08 DOI: 10.1109/TCYB.2026.3689903

Wenhui Liu, Qian Ma, Shengyuan Xu

引用次数: 0

Maximum-Return-Driven Consensus Framework With Internal-External Compensation in Undirected Collaboration Network. 无向协作网络中具有内外补偿的最大收益驱动共识框架。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-05 DOI: 10.1109/TCYB.2026.3688527

Jie Yang, Jian Wu, Zhaoguang Zhu, Mingshuo Cao, Enrique Herrera-Viedma

{"title":"Maximum-Return-Driven Consensus Framework With Internal-External Compensation in Undirected Collaboration Network.","authors":"Jie Yang, Jian Wu, Zhaoguang Zhu, Mingshuo Cao, Enrique Herrera-Viedma","doi":"10.1109/TCYB.2026.3688527","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3688527","url":null,"abstract":"In group decision-making (GDM) involving decision-makers (DMs) with heterogeneous interests and responsibilities, such as transboundary watershed governance, consensus formation is fundamentally return-driven. This study develops a directionally asymmetric maximum-return consensus model (MRCM) with nonnegative return constraints, which shifts from a moderator-cost perspective to an individual-return perspective. To this end, a feasibility diagnosis approach integrating the minimum-slack feasibility checking model (MSFCM) and the relaxed MRCM is proposed to determine whether the MRCM is feasible. When it is not feasible, it further reveals whether the infeasibility originates from individual- or collective-level return deficits. Based on the diagnosis results, a twofold adaptive consensus framework is further developed: 1)internal compensation-driven feedback is applied when individual return deficits coexist with a nonnegative total cooperative return, reallocating surplus via an asymmetric Nash bargaining game (ANBG) without modifying the relaxed consensus outcome and 2)external compensation-driven feedback is activated when the total cooperative return is negative, with the moderator providing the minimum compensation to ensure consensus with nonnegative returns. The novelty of this work lies in developing a unified return-driven consensus mechanism governed by feasibility diagnosis by refining the new return formulation and compensation scheme. A numerical study based on the Dongjiang River Basin demonstrates that the proposed framework adaptively selects compensation strategies and effectively enhances consensus feasibility and stability.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147837294","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

Attitude-Position Integrated Control for Spacecraft Formation With Predefined-Time Convergence. 具有预定义时间收敛的航天器编队姿态-位置综合控制。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-04 DOI: 10.1109/TCYB.2026.3678925

Yu-Yao Wu, Yaoyuan Zeng, Yuanyuan Zhang, Hui-Jie Sun

{"title":"Attitude-Position Integrated Control for Spacecraft Formation With Predefined-Time Convergence.","authors":"Yu-Yao Wu, Yaoyuan Zeng, Yuanyuan Zhang, Hui-Jie Sun","doi":"10.1109/TCYB.2026.3678925","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3678925","url":null,"abstract":"This article investigates the attitude-position integrated control of spacecraft formation in the presence of inertia uncertainties and external disturbances. The mathematical model of the spacecraft formation attitude-position integrated control system is established using dual quaternion representation. A novel predefined-time extended state observer (ESO) is developed to estimate the total perturbation, which includes both inertial uncertainties and external disturbances in the system model. Compared with existing predefined-time ESOs, the proposed observer eliminates redundant differential terms and provides a more rigorous theoretical proof. Based on the estimates obtained from this predefined-time ESO, a predefined-time attitude-position integrated control scheme for spacecraft formation described by dual quaternions is proposed for the first time. Furthermore, the closed-loop system's predefined-time stability is proven using Lyapunov stability theory. Finally, a series of comparative simulations validates the performance and effectiveness of the proposed predefined-time ESO and the presented control strategy.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147837330","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

Resilience Analysis of Closed-Loop Multiagent Systems Under Replay Attacks in the Context of Consensus Tracking. 共识跟踪下闭环多智能体系统在重放攻击下的弹性分析。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-04 DOI: 10.1109/TCYB.2026.3680883

Youqing Wang, Yiheng Shangguan, Xiaole Yang, Wenjing He, Yukun Shi

{"title":"Resilience Analysis of Closed-Loop Multiagent Systems Under Replay Attacks in the Context of Consensus Tracking.","authors":"Youqing Wang, Yiheng Shangguan, Xiaole Yang, Wenjing He, Yukun Shi","doi":"10.1109/TCYB.2026.3680883","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3680883","url":null,"abstract":"At present, extensive research efforts have been devoted to investigating network attacks on control systems. However, comparatively limited attention has been directed toward the resilience of multiagent systems (MASs) under such attacks, particularly in the case of replay attacks. Addressing the challenge of resilience analysis in strict-feedback nonlinear MASs under replay attacks, this article proposes a dynamic stability analysis method based on a classical distributed adaptive consensus control framework. To evaluate the resilience of the MASs in the context of aperiodic replay attacks, a dynamic compact set model is designed as a resilience metric. An iterative algorithm is then developed to compute the upper bound of tracking error jump at the beginning and end of the attack. In the scenario where the control signals under replay attacks cannot be explicitly modeled, this study derives an upper bound on the variation of the tracking error during the attack period using the Lyapunov stability analysis. It is proven that resilience can be maintained when the resting time between two consecutive replay attacks satisfies a given sufficient condition. Finally, simulation results illustrate the effectiveness of the proposed analysis method.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147837292","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

Intelligent Constrained Zero-Sum Game Design With Advanced Critic Learning Including a Two-Area Power System Application. 基于高级批评学习的智能约束零和博弈设计，包括两区电力系统的应用。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-04 DOI: 10.1109/TCYB.2026.3687128

Menghua Li, Ding Wang, Junfei Qiao

{"title":"Intelligent Constrained Zero-Sum Game Design With Advanced Critic Learning Including a Two-Area Power System Application.","authors":"Menghua Li, Ding Wang, Junfei Qiao","doi":"10.1109/TCYB.2026.3687128","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3687128","url":null,"abstract":"In this article, an advanced critic learning technique is established to handle the continuous-time (CT) multiplayer zero-sum game (MZSG) problem with asymmetric constraints. To begin with, a novel asymmetric constraint algorithm is presented, which relaxes the restrictions on the control matrices compared to prior related studies. Ulteriorly, the Hamilton-Jacobi-Isaacs equation, the optimal controls, and the worst disturbances are deduced for asymmetric constrained MZSGs (ACMZSGs). Since the acquired Hamilton-Jacobi-Isaacs equation is intractable to solve, an advanced critic learning scheme is built to attain the approximations of the optimal controls and the worst disturbances. It is noteworthy that this article develops a new weight tuning rule to lower the need for the initial admissible controls. Immediately after that, the stability analysis of the control system is given. In the end, the load frequency control problem for a two-area power system with linear dynamics is considered, and the simulation for a nonlinear system is performed to test the feasibility of the suggested control scheme. In particular, comparative experiments are established to further demonstrate the efficacy of the proposed weight tuning rule and the applicability of the proposed asymmetric constraint algorithm.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147837308","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 Prediction Model Integrating Adaptive-Network-Based Fuzzy Inference System and Fuzzy C-Mean Clustering. 基于自适应网络的模糊推理系统与模糊c均值聚类相结合的预测模型。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-04 DOI: 10.1109/TCYB.2026.3688199

Rongtao Zhang, Xueling Ma, Weiping Ding, Witold Pedrycz, Jianming Zhan

{"title":"A Prediction Model Integrating Adaptive-Network-Based Fuzzy Inference System and Fuzzy C-Mean Clustering.","authors":"Rongtao Zhang, Xueling Ma, Weiping Ding, Witold Pedrycz, Jianming Zhan","doi":"10.1109/TCYB.2026.3688199","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3688199","url":null,"abstract":"Multivariate prediction is a crucial tool for control system design, allowing for accurate prediction and control of system states by modeling the relationships between multiple input and output variables. However, existing models often rely on dimensionality reduction techniques to manage high-dimensional data, which can lead to the loss of valuable information. Combined prediction models (CPMs) have gained significant attention for their ability to integrate the strengths of multiple predictors, thereby improving prediction accuracy. Nevertheless, most current CPMs overlook the relationship between each predictor's output and the predicted sequences, resulting in suboptimal performance. To address these challenges, we propose an adaptive-network-based fuzzy inference system CPM (ANFIS-CPM) enhanced by an improved fuzzy $C$ -means (FCM) clustering, termed ANFIS-CPM-FCM. Our approach defines a similarity metric to quantify feature relationships, enhances traditional FCM to automatically determine the optimal number of clusters based on a density clustering model, trains separate ANFIS models on each cluster, and aggregates predictions by considering the relationships between each predictor's output and the predicted sequences. Extensive comparative and ablation experiments on six publicly available datasets demonstrate that our ANFIS-CPM-FCM outperforms existing methods in terms of prediction accuracy and robustness, highlighting the benefits of integrating improved clustering with adaptive fuzzy inference systems.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147837283","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-Sampling-Based Event-Triggered Control for Switching Reaction-Diffusion Neural Networks. 基于随机抽样的切换反应扩散神经网络事件触发控制。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-04 DOI: 10.1109/TCYB.2026.3688326

Jinnan Luo, Jun Cheng, Wanying Wei, Leszek Rutkowski, Huaicheng Yan, Jinde Cao, Yuanyuan Shen

引用次数: 0

HEQP: A Hypergraph Neural Network-Based Evolutionary Method for Large-Scale QCQPs. HEQP：基于超图神经网络的大规模qcqp进化方法。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-01 DOI: 10.1109/TCYB.2026.3651858

Zhixiao Xiong, Huigen Ye, Hua Xu, Carlos A Coello Coelle

{"title":"HEQP: A Hypergraph Neural Network-Based Evolutionary Method for Large-Scale QCQPs.","authors":"Zhixiao Xiong, Huigen Ye, Hua Xu, Carlos A Coello Coelle","doi":"10.1109/TCYB.2026.3651858","DOIUrl":"10.1109/TCYB.2026.3651858","url":null,"abstract":"Machine learning-based optimization frameworks have attracted increasing attention for accelerating the solution of large-scale quadratically constrained quadratic programs (QCQPs) by exploiting shared problem structure across instances. However, existing machine learning (ML) frameworks often rely on the assumption of parametric models and large-scale solvers. This article introduces HEQP, a hypergraph neural network-based evolutionary optimization framework for large-scale QCQPs. This framework features two main components: 1) hypergraph-based neural prediction, which predicts optimal solutions for QCQPs without assumptions of models; and 2) evolutionary large neighborhood search (Evo-LNS), which employs a McCormick relaxation-based repair strategy to search and apply crossover on neighborhood solutions using a small-scale solver. We further show that our framework is equivalent to the interior-point method (IPM), a polynomial-time algorithm, for quadratic programming. Experiments on two types of benchmark problems and 13 large-scale real-world instances from the QPLIB illustrate that our framework outperforms state-of-the-art solvers (including Gurobi, SCIP, and SHOT) in both solution quality and time efficiency, highlighting the efficiency of ML-based optimization frameworks for QCQPs.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":"2663-2676"},"PeriodicalIF":10.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131601","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

VL-HTR: Learning Human-Target Representation From Vision-Language Model. VL-HTR：从视觉语言模型学习人-目标表示。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-01 DOI: 10.1109/TCYB.2026.3659335

Binglu Wang, Chenxi Guo, Jingyi Cui, Haisheng Xia, Guangyu Guo, Zhijun Li

{"title":"VL-HTR: Learning Human-Target Representation From Vision-Language Model.","authors":"Binglu Wang, Chenxi Guo, Jingyi Cui, Haisheng Xia, Guangyu Guo, Zhijun Li","doi":"10.1109/TCYB.2026.3659335","DOIUrl":"10.1109/TCYB.2026.3659335","url":null,"abstract":"Human-gaze-target prediction aims to predict the target point or object that humans are looking at in images. However, existing methods predominantly rely on vision-only features, which often struggle to capture the semantic context of small or occluded objects and lack explicit priors for precise head direction regression, leading to slow convergence and suboptimal performance. Therefore, we introduce VL-HTR, a novel vision-language learning method for human-target representation, which integrates multimodal knowledge from vision-language models (VLMs) to construct robust human-target relationships. Unlike traditional approaches, extracting multimodal features via pretrained VLMs enhances the model's grasp of human-target knowledge through the learnable target class and direction context. Then, a language-guided query alignment (LQA) module is introduced to improve the semantic-aware object representation capability through vision-language query alignment. Finally, to accelerate the gaze point regression learning process, we design a language-guided direction prediction (LDP) module to introduce multimodal human gaze direction priors, thereby facilitating the human-target relationship construction. Extensive validations across two distinct tasks, i.e., gaze object prediction (GOP) and gaze target estimation, involving five challenging benchmarks, demonstrating that VL-HTR achieves superior performance and much faster training convergence.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":"2638-2649"},"PeriodicalIF":10.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146219732","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