IEEE Transactions on Cybernetics最新文献_第2页

Robotic Assistive Optimization and Control Using Neural Dynamics and Adaptive Neural Network. 基于神经动力学和自适应神经网络的机器人辅助优化与控制。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-01 DOI: 10.1109/TCYB.2026.3659300

Chao Cun, Liangrui Xu, Guoxin Li, Zhijun Li, Yu Kang

{"title":"Robotic Assistive Optimization and Control Using Neural Dynamics and Adaptive Neural Network.","authors":"Chao Cun, Liangrui Xu, Guoxin Li, Zhijun Li, Yu Kang","doi":"10.1109/TCYB.2026.3659300","DOIUrl":"10.1109/TCYB.2026.3659300","url":null,"abstract":"Humans can naturally learn and adapt to walking patterns in a variety of terrains. To simulate this learning characteristic, this article introduces a neural dynamics-based impedance optimization and trajectory adaptation approach for our designed soft exosuit, with a dual-driven configuration to assist both ankles of individuals. This method adaptively learns the impedance of the human ankle joint using measured interaction forces and dynamically adjusts trajectories to align with real-time human-robot interaction. Additionally, an adaptive control framework integrating neural dynamics-based optimization with several adaptive laws is developed to achieve stable tracking of updated reference trajectories, with Lyapunov stability analysis confirming uniform ultimate boundedness (UUB) of the closed-loop system. The designed controller offers the benefit of concurrently addressing trajectory adaptation, force control, and impedance tuning for soft exosuits. Experimental validation on human subjects across various terrains demonstrates that the proposed method reduces maximum trajectory tracking error to 0.016 rad (lower than PID and ADRC controllers) and enables impedance parameters to converge within 3 gait cycles. The controller concurrently addresses trajectory adaptation, force control, and impedance tuning, offering a lightweight (8 kg) and wearability-optimized solution for walking assistance.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":"2529-2542"},"PeriodicalIF":10.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146194698","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 Iterative Learning Reliable Control of Nonrepetitive Systems With Multiple Iteration-Varying Parametric Uncertainties. 多迭代变参数不确定性非重复系统的自适应迭代学习可靠控制。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-01 DOI: 10.1109/TCYB.2026.3661168

Yong Chen, Deqing Huang, Xuefang Li

{"title":"Adaptive Iterative Learning Reliable Control of Nonrepetitive Systems With Multiple Iteration-Varying Parametric Uncertainties.","authors":"Yong Chen, Deqing Huang, Xuefang Li","doi":"10.1109/TCYB.2026.3661168","DOIUrl":"10.1109/TCYB.2026.3661168","url":null,"abstract":"The repetitiveness prerequisite of iterative learning control has always been the main obstacle to promoting its practical applications. In this article, a novel adaptive iterative learning reliable control scheme is proposed for the nonrepetitive systems with multiple iteration-varying parametric uncertainties, where actuator faults and state delays are considered simultaneously. During the design of the controller, the class- $k_{infty } $ function is leveraged to dispose of the unmodeled lumps of systems through neural networks, and the transformation of control signals is established to compensate for the negative impact of the inefficient actuator. The technical features of our approach lie in an innovative parametric estimation mechanism that integrates the hyperbolic tangent function and an auxiliary sequence is presented to accommodate the nonrepetitive uncertainties, thus achieving the zero-error convergence of output. As the main merits, the proposed control scheme is promising to manifest better performance and practicality than the existing methods, owing to the weak assumptions on the system dynamics, the little prior knowledge of parametric uncertainties, and the strong learning ability of the controller.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":"2625-2637"},"PeriodicalIF":10.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146194532","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

LASFNet: A Lightweight Attention-Guided Self-Modulation Feature Fusion Network for Multimodal Object Detection. LASFNet：用于多模态目标检测的轻量级注意力引导自调制特征融合网络。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-01 DOI: 10.1109/TCYB.2025.3650459

Lei Hao, Lina Xu, Chang Liu, Yanni Dong

{"title":"LASFNet: A Lightweight Attention-Guided Self-Modulation Feature Fusion Network for Multimodal Object Detection.","authors":"Lei Hao, Lina Xu, Chang Liu, Yanni Dong","doi":"10.1109/TCYB.2025.3650459","DOIUrl":"10.1109/TCYB.2025.3650459","url":null,"abstract":"Effective deep feature extraction via feature-level fusion is crucial for multimodal object detection. However, previous studies often involve complex training processes that integrate modality-specific features by stacking multiple feature-level fusion units, leading to significant computational overhead. To address this issue, we propose a lightweight attention-guided self-modulation feature fusion network (LASFNet). The LASFNet adopts a single feature-level fusion unit to enable high-performance detection, thereby simplifying the training process. The attention-guided self-modulation feature fusion (ASFF) module in the model adaptively adjusts the responses of fused features at both global and local levels, promoting comprehensive and enriched feature generation. Additionally, a lightweight feature attention transformation module (FATM) is designed at the neck of LASFNet to enhance the focus on fused features and minimize information loss. Extensive experiments on three representative datasets demonstrate that our approach achieves a favorable efficiency-accuracy tradeoff. Compared to state-of-the-art methods, LASFNet reduced the number of parameters and computational cost by as much as 90% and 85%, respectively, while improving detection accuracy mean average precision (mAP) by 1%-3%. The code will be open-sourced at https://github.com/leileilei2000/LASFNet.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":"2516-2528"},"PeriodicalIF":10.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145989039","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

Max-Min Robust Unsupervised Feature Selection via Sparse Subspace. 基于稀疏子空间的最大最小鲁棒无监督特征选择。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-05-01 DOI: 10.1109/TCYB.2026.3656518

Sisi Wang, Feiping Nie, Zheng Wang, Rong Wang, Zhensheng Sun, Xuelong Li

{"title":"Max-Min Robust Unsupervised Feature Selection via Sparse Subspace.","authors":"Sisi Wang, Feiping Nie, Zheng Wang, Rong Wang, Zhensheng Sun, Xuelong Li","doi":"10.1109/TCYB.2026.3656518","DOIUrl":"10.1109/TCYB.2026.3656518","url":null,"abstract":"Feature selection is one of the hot issues in machine learning. It reduces storage pressure by effectively screening features and has become a very practical data preprocessing method. At present, most feature selection algorithms apply $ell _{2,1}$ -norm on the transformation matrix to calculate the scores for all features and then select appropriate features according to these scores. But their sparsity is limited, and meaningless regularization parameters increase the cost, making it prone to falling into local optimum. To solve the above difficulties, this article proposes a novel max-min robust unsupervised feature selection via sparse subspace (MMRUFS), which considers both the reconstruction term and variance term of data, so that the model can not only fully retain the original information of data, but also make the data more dispersed. Second, $ell _{2,0}$ -norm constraint is used on the transformation matrix to directly select the optimal feature subset, avoiding the fine-tuning of regularization parameters. To enhance the robustness, MMRUFS carefully designs mark weight vector to make the model treat normal samples and outliers differently and achieves the effect of anomaly detection. Finally, MMRUFS is solved by designing the surrogate matrix, and its convergence is strictly guaranteed, experimental results reveal that MMRUFS outperforms other feature selection algorithms on multiple real-world datasets.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":"2611-2624"},"PeriodicalIF":10.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146219704","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

Finite-Time Intermittent Anti-Disturbance Control for Discrete-Time Switched Systems With Stochastic Gain Fluctuations: Partial Information Loss Case. 具有随机增益波动的离散时间切换系统的有限时间间歇抗干扰控制：部分信息损失情况。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-04-30 DOI: 10.1109/TCYB.2026.3683961

Kui Ding, Quanxin Zhu, Wei Xing Zheng

{"title":"Finite-Time Intermittent Anti-Disturbance Control for Discrete-Time Switched Systems With Stochastic Gain Fluctuations: Partial Information Loss Case.","authors":"Kui Ding, Quanxin Zhu, Wei Xing Zheng","doi":"10.1109/TCYB.2026.3683961","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3683961","url":null,"abstract":"This study focuses on the finite-time (FITI) intermittent anti-disturbance control problem of discrete-time switched systems with multiple disturbances and stochastic gain fluctuations. Different from the existing switching system works, a permissible edge-dependent average dwell time mechanism is developed instead of the existing common mode-dependent average dwell time (MDADT) mechanism, which is more flexible and has a wider range of applications. Subsequently, the switching system studied in this study considers not only external disturbance but also malicious cyberattacks, which is more in line with the actual background and has more theoretical significance and practical research value. Furthermore, an intermittent composite anti-disturbance strategy is designed to replace the existing continuous anti-disturbance control strategy based on fully measurable information in the case that external disturbance and malicious cyberattacks are prone to inducing partial information loss. More importantly, a novel FITI $mathscr {H}_{infty } $ stabilization criterion for control and a highly practical optimization algorithm are presented, which can effectively reduce the control costs. Finally, the results of the developed FITI intermittent anti-disturbance control scheme are verified by using a real two-shaft turbofan engine (TSTE) model.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147814415","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

Individual Linguistic Granular Computing: A Granulation-Degranulation-Based Approach. 个体语言颗粒计算：一种基于颗粒-脱颗粒的方法。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-04-29 DOI: 10.1109/TCYB.2026.3686428

Ting Huang, Qiang Zhang, Witold Pedrycz, Shanlin Yang

{"title":"Individual Linguistic Granular Computing: A Granulation-Degranulation-Based Approach.","authors":"Ting Huang, Qiang Zhang, Witold Pedrycz, Shanlin Yang","doi":"10.1109/TCYB.2026.3686428","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3686428","url":null,"abstract":"This study proposes a granulation-degranulation-based approach for individual linguistic granular computing that addresses two types of uncertainty: ambiguity in semantic representation and contextual variability in semantic choices. Current studies typically use intervals as the formalism for granulation, with random sampling on these intervals to perform degranulation. In contrast, this study employs a more versatile probability-sampling-based degranulation method that uses truncated and mirrored power-law distributions. This method is based on three key assumptions derived from the central limit theorem and establishes a relationship between the power-law index and the uncertainty in interpreting linguistic terms. Moreover, to enhance alignment with human perception, this study designs an optimization framework that integrates interval-based granulation with the proposed probability-sampling-based degranulation method. The effectiveness and practicality of the proposed approach are validated through an experimental study on the risk assessment of aircraft engines. The results of the experiments on the semantic analysis of online reviews demonstrate that the proposed approach achieves superior performance, as evidenced by comparisons with other computing-with-words models and seven large language models.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147769990","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

Finite-Time Model-Free Adaptive Consensus Control for Unknown Nonlinear Multiagent Systems With Experimental Validation. 未知非线性多智能体系统的有限时间无模型自适应一致控制及实验验证。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-04-29 DOI: 10.1109/TCYB.2026.3683959

Yongpeng Weng, Hongye Chen, Jinyao Cheng, Huaicheng Yan, Wenhai Qi

{"title":"Finite-Time Model-Free Adaptive Consensus Control for Unknown Nonlinear Multiagent Systems With Experimental Validation.","authors":"Yongpeng Weng, Hongye Chen, Jinyao Cheng, Huaicheng Yan, Wenhai Qi","doi":"10.1109/TCYB.2026.3683959","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3683959","url":null,"abstract":"Almost all existing model-free adaptive consensus control (MFACC) strategies for nonlinear multiagent systems (MASs) have been limited to asymptotic tracking performance. To address this limitation and unify prior theoretical frameworks, this article proposes a novel finite-time MFACC (FMFACC) approach for rapid consensus tracking in nonlinear MASs with completely unknown dynamics. A new finite-time consensus tracking error is first constructed by incorporating a variable proportional coefficient with an adjacent maximum tracking error factor, ensuring finite-time convergence while accounting for output couplings. Building on this and by establishing time-varying data models that capture the unknown nonlinear dynamics, a distributed finite-time consensus tracking control law with a data-driven adaptive gain matrix is developed, enabling model-free fast coordination of all agents along a predefined trajectory. Furthermore, by employing an equivalent system transformation strategy, the relationship between the resulting closed-loop nonlinear FMFACC system and its conventional linear counterpart is rigorously analyzed, proving asymptotically finite-time consensus tracking despite unknown system dynamics and output couplings. Finally, simulation and experimental studies conclusively demonstrate the superior performance of the proposed FMFACC approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147770048","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 Constrained Nonsmooth Minimax Optimization in Two Multiagent Systems: An Adaptive Penalty Approach. 两个多智能体系统的分布约束非光滑极小极大优化：一种自适应惩罚方法。

IF 10.5 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-04-29 DOI: 10.1109/TCYB.2026.3685162

Binxin Hu, Shu Liang

{"title":"Distributed Constrained Nonsmooth Minimax Optimization in Two Multiagent Systems: An Adaptive Penalty Approach.","authors":"Binxin Hu, Shu Liang","doi":"10.1109/TCYB.2026.3685162","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3685162","url":null,"abstract":"Recently, many efficient algorithms for minimax problems have been proposed, but there are relatively few methods for solving nonsmooth constrained minimax problems. This article focuses on developing a distributed algorithm to address this underexplored class of constrained nonsmooth minimax optimization challenges. To be specific, the distributed nonsmooth convex-concave minimax problem with inequality constraints for multiagent systems is considered, where the two subsystems have opposite objectives, minimization and maximization, respectively. Individual agents cooperate with their neighbors in their own subsystem and compete with agents in the other subsystem, and agents have only partial knowledge of the other subsystem. We propose a distributed continuous-time penalty-based algorithm that adaptively determines appropriate penalty gains. In particular, the proposed algorithm is an adaptive strategy that eliminates Lagrangian multipliervariables and avoids explicit estimation of exact penalty parameters. Furthermore, we prove that the state solution of our algorithm achieves group consensus and converges to the saddle point of the minimax problem. Finally, numerical simulations demonstrate the effectiveness and superiority of the algorithm.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147769963","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 Comprehensive Framework for Generating Adaptive Arbitrarily Predefined-Time Convergent RNNs for Dynamic Zero-Finding Problem Applied to Circuits and Robotics. 一种用于电路和机器人动态寻零问题的自适应任意预定义时间收敛rnn生成综合框架。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-04-28 DOI: 10.1109/tcyb.2026.3681029

Boyu Zheng,Daxuan Yan,Chunquan Li,Sichen Zhang,Zhijun Zhang,Xiao-Hu Zhou,Junzhi Yu,P X Liu

{"title":"A Comprehensive Framework for Generating Adaptive Arbitrarily Predefined-Time Convergent RNNs for Dynamic Zero-Finding Problem Applied to Circuits and Robotics.","authors":"Boyu Zheng,Daxuan Yan,Chunquan Li,Sichen Zhang,Zhijun Zhang,Xiao-Hu Zhou,Junzhi Yu,P X Liu","doi":"10.1109/tcyb.2026.3681029","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3681029","url":null,"abstract":"Recurrent neural networks (RNNs) with predefined-time convergence capabilities are among the most powerful solvers for time-varying zero-finding problems (TVZFPs). However, a comprehensive design framework for such neural networks has not yet been well established. To address this gap, this article presents a comprehensive framework for generating a series of adaptive arbitrarily predefined-time convergent RNNs (A-APTC-RNNs). Compared with most existing RNNs, the A-APTC-RNNs generated using the proposed comprehensive framework exhibit the following distinctive features: 1)owing to a novel piecewise evolution formula, their convergence time can be arbitrarily predefined; 2)owing to a proportional-integral-derivative regulatory mechanism, they achieve lower steady-state residual errors after convergence; and 3)owing to a novel adaptive parameter initialization scheme, they are able to automatically determine their own model parameters. Theoretical analysis rigorously demonstrates the stability and arbitrarily predefined-time convergence (APTC) capability of the A-APTC-RNNs. Various experiments (i.e., numerical simulations, alternating-current estimation, chaotic synchronization of Chua's circuit, and motion generation for dual-arm robots) demonstrate the state-of-the-art convergence performance of the A-APTC-RNNs generated by the proposed comprehensive framework.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"21 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147754615","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 Output Consensus Control for Unknown Discrete-Time Multiagent Systems With Sensor Uncertainties Under Stochastic Communication Protocol 随机通信协议下具有传感器不确定性的未知离散多智能体系统自适应输出一致性控制

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-04-27 DOI: 10.1109/tcyb.2026.3685721

Yuqian Lin, Yajie Ma, Bin Jiang, Liyan Wen

引用次数: 0