IEEE Transactions on Cybernetics最新文献

{"title":"Distributed Dynamic Event-Triggered Formation Tracking Control for Multiagent Systems via a Unified Update Mechanism.","authors":"Shuangsi Xue, Zihang Guo, Hui Cao, Badong Chen","doi":"10.1109/TCYB.2026.3731639","DOIUrl":"10.1109/TCYB.2026.3731639","url":null,"abstract":"<p><p>This article investigates the distributed formation tracking control of nonlinear multiagent systems (MASs) in the presence of unknown dynamics and external disturbances, with a particular focus on communication efficiency. To simultaneously address accurate estimation and communication savings, a unified dynamic event-triggered framework integrating observer and controller updates is proposed. In particular, an event-triggered extended state observer (ETESO) enhanced by a bias radial basis function neural network (BRBFNN) is developed, capable of capturing system uncertainties and external disturbances. Subsequently, a distributed dynamic event-triggered control scheme is designed, enabling each agent to update its ETESO and controller simultaneously and adaptively through dynamic triggering thresholds. Rigorous theoretical analysis is provided to confirm the semiglobal uniform ultimate boundedness (SGUUB) of the closed-loop system. Compared with existing fixed-threshold or controller-only triggering schemes, simulation results confirm that the proposed unified update framework simplifies system architecture while significantly enhancing estimation accuracy, tracking performance, and communication efficiency.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149030625","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}

{"title":"A Distributed Monitoring Framework for Large-Scale Industrial Processes Based on Differential Grouping and Hierarchical Cooperative Modeling.","authors":"Xi Tu, Xuefeng Yan","doi":"10.1109/TCYB.2026.3732656","DOIUrl":"10.1109/TCYB.2026.3732656","url":null,"abstract":"<p><p>Modern large-scale industrial processes, characterized by high dimensionality, strong nonlinearity, and complex spatiotemporal couplings, pose significant challenges to traditional monitoring methods. To address these challenges, this article proposes a novel distributed monitoring framework: differential grouping and hierarchical cooperative monitoring (DG-HCM). First, a differential grouping (DG) strategy leverages deep feature interactions to adaptively decompose the process into functionally cohesive sub-blocks. Second, a hierarchical cooperative monitoring system is constructed upon this decomposition: a multiscale convolutional autoencoder (MSCAE) captures intrablock dynamics; canonical correlation analysis (CCA) with a spatiotemporal context monitors interblock collaborations; and a global statistic integrating all CCA canonical scores assesses system-wide coordination. Finally, all local statistics are fused into a unified decision index via Bayesian inference, while a hierarchical contribution analysis enables precise traceability from faulty sub-blocks to root-cause variables. Extensive validations on a numerical simulation, the Tennessee Eastman (TE) process, and a real-world industrial wastewater treatment plant (WWTP) demonstrate that the proposed framework outperforms advanced benchmarks in both monitoring performance and diagnostic interpretability.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149030580","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}

{"title":"Distributed Cooperative Control Path Planning for UAV Formation Based on Ordered Trigger Communication.","authors":"Chao Song, Hao Li, Yang She, Bo Li, Jiacun Wang, Qingfu Zhang","doi":"10.1109/TCYB.2026.3723940","DOIUrl":"10.1109/TCYB.2026.3723940","url":null,"abstract":"<p><p>To address issues such as control instability and imbalance caused by communication constraints, this article proposes a distributed cooperative control method for multi-uncrewed aerial vehicle (UAV) formations based on communication-ordered triggering. First, to address the limited interactions caused by obstacle occlusion and other factors, a nonoverlapping conflict-staggered communication triggering mechanism is introduced. By incorporating intermittent neighboring communication principles, UAVs maximize information sharing within a limited communication window, effectively resolving issues such as channel conflicts and resource wastage caused by periodic broadcasting in existing communication methods. Next, a gradient-adaptive anti-saturation constraint model is developed to resolve the problems of large control parameter adjustments and slow convergence inherent in traditional anti-saturation methods. In addition, an optimized distributed cooperative orientation control strategy is constructed by integrating position and attitude information, preventing formation instability caused by the reliance on a single observation under interaction constraints. This approach significantly reduces the communication burden while enhancing the stability and adaptability of multi-UAV control. The experimental results show that the proposed communication-ordered triggering multi-UAV formation distributed cooperative control strategy exhibits global asymptotic stability. Compared to the adaptive optimization control method, this approach can maintain effective target tracking and formation stability under communication constraints. Moreover, it provides the UAV swarm with the ability for autonomous target tracking and formation reconfiguration. The communication frequency is significantly reduced compared to the cooperative event-triggered mechanism (CETM) and periodic event-triggered mechanism (PETM) methods, thereby reducing communication bandwidth usage, which aligns with the practical flight requirements of multi-UAV systems.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149030564","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}

{"title":"Maximum Correntropy Kalman Filter-Based Robust Model-Free Adaptive Control for MIMO Nonlinear Systems Under Non-Gaussian Noise.","authors":"Guojie Li, Ping Zhou, Tao Yang, Tianyou Chai","doi":"10.1109/TCYB.2026.3730841","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3730841","url":null,"abstract":"<p><p>In this article, a robust model-free adaptive control (MFAC) algorithm is proposed based on the maximum correntropy Kalman filter (MCKF) for a class of multiple-input-multiple-output discrete-time nonlinear systems subject to non-Gaussian noise. The proposed algorithm relies solely on input-output data without requiring any model information, effectively suppressing output fluctuations caused by non-Gaussian noise while ensuring satisfactory tracking performance. First, a modified linear data model is constructed using partial-form dynamic linearization (DL). Leveraging this model, a fixed-point iteration-based MCKF is designed to derive posterior output estimates characterized by reduced uncertainty and enhanced smoothness. Subsequently, a cost function involving the posterior tracking error and its difference term is formulated using the posterior output estimates, based on which a robust MFAC algorithm is derived. Furthermore, the mean-square exponential boundedness of the posterior output estimation error and the boundedness of the expectation of the tracking error norm are rigorously established. Finally, the effectiveness and robustness of the proposed algorithm are verified through a numerical simulation and a blast furnace (BF) ironmaking data-based case study.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038430","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}

{"title":"Predefined-Time Output Tracking Control of Nonlinear Systems With Uncertain Parameters Under Denial-of-Service Attacks.","authors":"Ting Liu, Shiwen Xie, Yongfang Xie, Tingwen Huang","doi":"10.1109/TCYB.2026.3733290","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3733290","url":null,"abstract":"<p><p>This article investigates the predefined-time output tracking control of nonlinear systems consisting of multiple output-coupled subsystems with uncertain parameters and subject to edge- and node-based denial-of-service (DoS) attacks. The main challenge lies in achieving predefined-time tracking in the presence of both unknown nonlinear dynamics and complex DoS attack patterns. To address this challenge, fuzzy neural networks (FNNs) are employed to approximate the unknown nonlinear dynamics, and an output-feedback controller is developed based on a generalized function. The proposed control scheme provides a unified treatment of edge- and node-based DoS attacks, under which the tracking error converges to zero within the specified time. In addition, sufficient conditions are established to guarantee the predefined-time output tracking of nonlinear systems with uncertain parameters under edge- and node-based DoS attacks. The continuity and boundedness of the control input are also rigorously guaranteed, which enhances the practical implementation of the proposed controller. Finally, numerical examples are conducted to validate the theoretical results and demonstrate the effectiveness of the proposed method under different attack and fault scenarios.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038371","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}

{"title":"Voting Consensus Incentive Mechanism Based on Deep Reinforcement Learning in Social Network Group Decision-Making","authors":"Bowen Zhang, Wenjun Huang, Qingxian An, Witold Pedrycz","doi":"10.1109/tcyb.2026.3734268","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3734268","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149019950","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}

{"title":"Flexible Performance Control Under Constrained Actuation for Nonsquare Nonlinear Systems With Controllability Relaxation.","authors":"Bing Zhou, Kai Zhao, Yongduan Song, Lihua Xie","doi":"10.1109/TCYB.2026.3731177","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3731177","url":null,"abstract":"<p><p>Prescribed performance control (PPC) for nonsquare nonlinear systems with unknown time-varying control gains and actuator constraints remains largely unexplored. This article addresses this problem by integrating a relaxed controllability condition with a flexible performance control framework based on saturation deficiency. Specifically, a refined matrix decomposition and transformation are employed to relax the controllability conditions for uncertain nonsquare nonlinear systems. Subsequently, the original system is augmented with constructed saturation auxiliary systems, which not only alleviate the effects of nonsmooth saturated inputs but also facilitate the backstepping control design under actuator saturation. To balance actuator constraints and performance requirements, we design a tunnel-shaped prescribed-time performance function (PTPF) equipped with saturation awareness capability. This function flexibly extends the performance boundaries during saturation, ensuring that the tracking error remains within the prescribed constraints while placing no restrictions on the saturation level. Both theoretical analysis and over-actuated spacecraft simulations confirm the proposed strategy's capability to achieve flexible prescribed tracking performance under actuator constraints.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038424","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}

{"title":"Secure Asynchronous Impulsive Control of Nonlinear Multiagent Systems Under Deception Attacks.","authors":"Yufan Chen, Yuchao Xiong, Mengqi Xue, Yue Song","doi":"10.1109/TCYB.2026.3731803","DOIUrl":"10.1109/TCYB.2026.3731803","url":null,"abstract":"<p><p>This article investigates the secure control problem of multiagent systems (MASs) with Lipschitz nonlinear dynamics under the asynchronous impulsive control framework. Random substitution deception attacks are modeled by Bernoulli variables and are assumed to occur in the controller or controller-to-actuator channel. By regarding the deception attack signal as a bounded external disturbance, the secure control problem is converted into the exponential input-to-state stability (EISS) problem of the derived consensus error system. A multisegmented Lyapunov function is designed to characterize the asynchronous impulsive dynamics. Under the impulse dwell-time condition, sufficient conditions are derived to guarantee EISS in mean square for MASs. Simulation results are provided to validate the proposed method and illustrate the relationship between the consensus target value and the network topology.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149030327","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}

{"title":"Practical Prescribed-Time Fully Actuated Tracking Control for 2-DOF Pneumatic Parallel Mechanism With Input Saturation.","authors":"Yipeng Cao, Li Li, Changchun Hua, Jiaping Qiang","doi":"10.1109/TCYB.2026.3732042","DOIUrl":"10.1109/TCYB.2026.3732042","url":null,"abstract":"<p><p>In this article, the attitude tracking control is investigated for a two-degree-of-freedom (2-DOF) pneumatic parallel mechanism subject to input saturation and unknown disturbances. First, joint estimation of unknown dynamics and disturbances is achieved by integrating a radial basis function (RBF) neural network into the design of a prescribed-time refined extended state observer (PTRESO). Next, a second-order auxiliary system is constructed to mitigate the effects of input saturation. A practical prescribed-time fully actuated control (PPTFAC) method is developed to simplify controller design and ensure that the tracking error reaches a preset accuracy within a prescribed settling time. Finally, the effectiveness and robustness of the proposed method are validated through simulations and experiments.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149030201","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}

{"title":"Vision-Based 6-DoF Grasp Pose Estimation for Robot Cloth Unfolding.","authors":"Domen Tabernik, Peter Nimac, Jan Jericevic, Danijel Skocaj, Andrej Gams","doi":"10.1109/TCYB.2026.3732543","DOIUrl":"10.1109/TCYB.2026.3732543","url":null,"abstract":"<p><p>Cloth manipulation is a challenging task due to the deformable and high-dimensional nature of cloth, which leads to complex interaction dynamics and perceptual ambiguity arising from frequent occlusions of critical visual cues, such as folds, edges, and grasp points. In this work, we tackle cloth unfolding using a regrasping-in-the-air strategy, where one manipulator holds the cloth while the other grasps it at an optimally selected point to unfold it. To this end, we propose center direction regression network (CeDiRNet)-6DoF, a deep learning framework that jointly predicts effective grasp points and the complete 6-DoF grasp pose from the observed cloth configuration. By integrating dense 3-D grasp regression with segmentation and sine-cosine-encoded Euler angles, the proposed method reliably estimates the grasp configuration that maximizes the unfolded cloth area. We extensively evaluated CeDiRNet-6DoF on a bimanual robotic setup within the ICRA 2024 Cloth Competition framework, achieving state-of-the-art performance. An ablation study further validates the benefits of key design components, including joint segmentation, background randomization, and image cropping. These results establish CeDiRNet-6DoF as a robust and versatile foundation for reliable robotic cloth manipulation in unstructured environments.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149030280","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}