IEEE Transactions on Cybernetics最新文献_第4页

Uncalibrated Model-Free Visual Servo Control for Robotic Endoscopic with RCM Constraint Using Neural Networks. 基于神经网络的RCM约束下机器人内窥镜无标定视觉伺服控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-09 DOI: 10.1109/tcyb.2025.3582866

Mengrui Cao,Lin Xiao,Qiuyue Zuo,Xiangru Yan,Linju Li,Xieping Gao

{"title":"Uncalibrated Model-Free Visual Servo Control for Robotic Endoscopic with RCM Constraint Using Neural Networks.","authors":"Mengrui Cao,Lin Xiao,Qiuyue Zuo,Xiangru Yan,Linju Li,Xieping Gao","doi":"10.1109/tcyb.2025.3582866","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3582866","url":null,"abstract":"With the advancement of robotic-assisted minimally invasive surgery, visual servo control has become a crucial technique for improving surgical outcomes. However, traditional visual servo methods often rely on precise kinematic models and camera calibration, limiting their generalizability. Considering these, this article proposes a novel uncalibrated model-free visual servo control scheme. Specifically, we introduce a Jacobian matrix and interaction matrix estimation method based on a gradient neural network (GNN), which enables online estimation by utilizing control signals and sensor outputs. Then, the estimated results are incorporated into a visual servo control framework that considers remote center of motion (RCM) constraint, joint-drift problem, and physical constraint, formulated as a quadratic programming (QP) problem. Subsequently, focusing on the joint limits and endoscope insertion depth constraint, we develop a nonpiecewise differentiable multilevel constraint handling technique. For the formulated QP problem, a predefined-time convergent error-regulating zeroing neural network (PTCER-ZNN) solver is designed, and we can derive the optimal control signals. Detailed theoretical analyses of the developed GNN estimation method and the PTCER-ZNN solver are provided. Simulation results demonstrate the effectiveness of the proposed scheme in image feature regulation and tracking tasks, exhibiting its advantages over existing approaches.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"4 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594438","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

Long-Term Prediction Model for Fuzzy Granular Time Series Based on Trend Filter Decomposition and Ensemble Learning. 基于趋势滤波分解和集成学习的模糊粒度时间序列长期预测模型。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-09 DOI: 10.1109/tcyb.2025.3582771

Chenglong Zhu,Xueling Ma,Weiping Ding,Witold Pedrycz,Jianming Zhan

{"title":"Long-Term Prediction Model for Fuzzy Granular Time Series Based on Trend Filter Decomposition and Ensemble Learning.","authors":"Chenglong Zhu,Xueling Ma,Weiping Ding,Witold Pedrycz,Jianming Zhan","doi":"10.1109/tcyb.2025.3582771","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3582771","url":null,"abstract":"In the realm of control theory, the complex task of long-term time series prediction has been profoundly transformed by the confluence of advancements in computer technology and machine learning. However, the application of fuzzy information granularity remains a significant challenge, primarily due to the potential for substantial data distortion. To address this limitation, we propose an innovative long-term prediction model based on granularity time series, which integrates l1-trend filter decomposition and integrated learning. The core of our model lies in a novel modal decomposition method that utilizes l1-trend filters and a validity function to meticulously extract valuable insights from the original time series, thereby enhancing the precision of data analysis while preserving the integrity of the original data. Furthermore, we introduce a groundbreaking formula to measure the similarity of fuzzy information granularity, classifying time series components into three distinct categories: trend, period, and noise. By applying distinct prediction strategies to each category, we construct an integrated learning model that leverages the strengths of each component. At the heart of our model is a multilinear information granularity prediction approach, which is based on trend time windows and utilizes the newly developed similarity measure. This method not only maintains the integrity of the original time series but also offers a more accurate representation of the similarity between information grains. Empirical results from publicly available datasets validate the superior performance of our proposed prediction model, demonstrating its potential to significantly enhance long-term time series prediction accuracy.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"11 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594436","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

H2-H∞ Composite Control for Singularly Perturbed Systems With Finite-Frequency Performances. 有限频率奇摄动系统的H2-H∞复合控制。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-09 DOI: 10.1109/tcyb.2025.3581280

Hongzheng Quan,Xiujuan Lu,Chenxiao Cai,Hong Lin,James Lam

引用次数: 0

Adaptive Safety-Based Tracking Control for Uncertain Robotic Systems With Input-Output Constraints: A Neural Network-Based Augmented High-Order Control Barrier Function Approach. 具有输入输出约束的不确定机器人系统自适应安全跟踪控制：一种基于神经网络的增广高阶控制障碍函数方法。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-09 DOI: 10.1109/tcyb.2025.3580085

Haijing Wang,Jinzhu Peng,Yaqiang Liu,Wei He,Yaonan Wang

{"title":"Adaptive Safety-Based Tracking Control for Uncertain Robotic Systems With Input-Output Constraints: A Neural Network-Based Augmented High-Order Control Barrier Function Approach.","authors":"Haijing Wang,Jinzhu Peng,Yaqiang Liu,Wei He,Yaonan Wang","doi":"10.1109/tcyb.2025.3580085","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3580085","url":null,"abstract":"This article investigates the trajectory tracking control of uncertain robotic systems with limited control torque input bounds and joint position constraints. A novel neural network-based augmented high-order control barrier function (NN-AHoCBF) is proposed to facilitate the tracking control strategy of uncertain robotic systems with input-output constraints, where the neural network (NN) is used to estimate uncertainties in the robotic system dynamics, and the bounds of NN approximation errors and NN weights are adapted in the high-order time derivative of the HoCBFs. The NN-AHoCBF is then derivated with a series of time-varying functions, and auxiliary systems are constructed to guarantee the time-varying functions to be HoCBFs. In this way, the control input of the robotic system is relaxed by adjusting the time-varying functions through the inputs of auxiliary systems in NN-AHoCBF barrier conditions. Also, the sufficient condition for the NN-AHoCBF is provided to adaptively ensure system safety. The adaptive safety-based tracking control method is designed based on NN-AHoCBF in quadratic program (QP) framework, which can not only satisfy input-output constraints simultaneously, but also achieve good robustness and tracking performance. A simulation example is performed on a two-DOF robotic mainpulator to verify the effectiveness of the developed controller.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"11 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594435","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 Model-Free Output-Feedback H∞ Parameterization Control Method With Unknown States Under Ill-Condition. 一种新的病态未知状态无模型输出反馈H∞参数化控制方法。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-09 DOI: 10.1109/tcyb.2025.3582874

Yanhong Luo,Shunwei Hu,Xiangpeng Xie,Huaguang Zhang

{"title":"A Novel Model-Free Output-Feedback H∞ Parameterization Control Method With Unknown States Under Ill-Condition.","authors":"Yanhong Luo,Shunwei Hu,Xiangpeng Xie,Huaguang Zhang","doi":"10.1109/tcyb.2025.3582874","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3582874","url":null,"abstract":"Developing model-free H∞ optimal control schemes in systems with unknown model parameters and unmeasurable states is challenging. In this article, an output-feedback (OPFB) suboptimal control scheme based on adaptive dynamic programming (ADP) is proposed to realize model-free H∞ control under uncertain disturbances. First, a free matrix is introduced to compute the suboptimal gain in the absence of an optimal OPFB gain, and a policy iterative algorithm is developed to solve for the suboptimal gain and shown to converge to a solution of the algebraic Riccati equation. In addition, a model-free ADP algorithm is proposed to realize online learning of control parameters without relying on system dynamics parameters. The Lanczos method is introduced to solve the ill-condition problem in the model-free algorithm solution. After that, the algorithm is further extended to the case where the system state is not measurable and parameterized reconstruction is performed using online input-output data. The results show that the proposed algorithm can realize model-free control with unknown parameters and unmeasurable states. The effectiveness of the proposed control scheme is simulated by an F-16 aircraft.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"34 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144594437","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 Generalized Udwadia-Kalaba Control Design With Speed Inequality Constraints. 具有速度不等式约束的广义Udwadia-Kalaba控制设计。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-09 DOI: 10.1109/tcyb.2025.3581228

Yuan Zhang,Qiying Li,Xin Chang,Tao Zhao,Chenming Li

引用次数: 0

cc-DRL: A Convex Combined Deep Reinforcement Learning Flight Control Design of a Morphing Quadrotor. 一种凸结合深度强化学习的变形四旋翼飞行控制设计。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-09 DOI: 10.1109/TCYB.2025.3580074

Tao Yang, Huai-Ning Wu, Jun-Wei Wang

{"title":"cc-DRL: A Convex Combined Deep Reinforcement Learning Flight Control Design of a Morphing Quadrotor.","authors":"Tao Yang, Huai-Ning Wu, Jun-Wei Wang","doi":"10.1109/TCYB.2025.3580074","DOIUrl":"https://doi.org/10.1109/TCYB.2025.3580074","url":null,"abstract":"In comparison to common quadrotors, the structure deformation of morphing quadrotors endows them with better flight performance but also results in more complex flight dynamics. Generally, it is extremely difficult or impossible for these morphing quadrotors to develop an accurate mathematical model that describes their complex flight dynamics. This fact leads to a particularly challenging situation, as the existing mature model-based flight control theory fails to address the flight control design issue of morphing quadrotors. By resorting to a combination of model-free control techniques e.g., deep reinforcement learning (DRL) and convex combination (CC) technique, a convex-combined-DRL (cc-DRL) flight control algorithm is proposed for flight trajectory tracking and attitude stabilization of a class of morphing quadrotors with arm-length deformation. In the proposed cc-DRL flight control algorithm, a proximal policy optimization algorithm is utilized to offline train the corresponding optimal flight control laws for some selected representative arm length modes. Hereby, a cc-DRL flight control scheme is constructed by the CC technique. Finally, simulation results are presented to show the effectiveness and merit of the proposed DRL flight control algorithm.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144600252","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 Generalized Nash Equilibrium Seeking for Linear Systems Over a Switching Network. 交换网络上线性系统的分布广义纳什均衡寻求。

IF 11.8 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-08 DOI: 10.1109/tcyb.2025.3579958

Xiongnan He,Zongli Lin

引用次数: 0

Domain Perturbation With Uncertainty for Bearing Fault Diagnosis Under Unseen Conditions. 未知条件下轴承故障诊断的不确定性域摄动。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-04 DOI: 10.1109/TCYB.2025.3581309

Yongyi Chen, Dan Zhang, Ruqiang Yan, Min Xie, Qi Xuan

{"title":"Domain Perturbation With Uncertainty for Bearing Fault Diagnosis Under Unseen Conditions.","authors":"Yongyi Chen, Dan Zhang, Ruqiang Yan, Min Xie, Qi Xuan","doi":"10.1109/TCYB.2025.3581309","DOIUrl":"https://doi.org/10.1109/TCYB.2025.3581309","url":null,"abstract":"Domain adaptation (DA) techniques are becoming increasingly proficient in cross-domain fault diagnosis tasks. However, DA-based methods are not always applicable due to the target domain data is not always accessible. Although there have been some interesting domain generalization methods for fault diagnosis under unseen conditions, most of them can only be used to mine the fault features on source domain distributions, and the improvement of model generalization performance is limited. To solve this problem, the multiplicative noise Gaussian perturbation strategy and the additive noise linear fusion strategy are proposed to capture fault information beyond source domain distributions. The former is used to randomly perturb feature statistics of multisource domains to simulate the uncertainty of domain shift, while the latter is used to perform the additive noise linear operation on feature statistics of multiple source domains to ensure the authenticity of the generated feature styles. Further, the feature statistics generated by both strategies are mixed with random convex weights to obtain new feature styles, achieving the best compromise between reliability and diversity. The network can learn more fault information from features with diversified styles. Extensive experimental results on both public and real datasets verify the effectiveness of our approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564739","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

Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model Predictive Control. 脑控轮式移动机器人：一种结合概率脑机接口和模型预测控制的框架。

IF 9.4 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2025-07-03 DOI: 10.1109/TCYB.2025.3580726

Xinyu Yu, Xiaojun Yu

{"title":"Brain-Controlled Wheeled Mobile Robots: A Framework Combining Probabilistic Brain-Computer Interface and Model Predictive Control.","authors":"Xinyu Yu, Xiaojun Yu","doi":"10.1109/TCYB.2025.3580726","DOIUrl":"https://doi.org/10.1109/TCYB.2025.3580726","url":null,"abstract":"Brain-controlled systems have experienced significant advancements in overall performance, largely driven by continuous optimization and innovation in electroencephalography (EEG) acquisition experimental paradigms and decoding algorithms. However, their applications still face challenges, including limited control precision and low efficiency. In this article, we focus on a wheeled mobile robot (WMR) as the control object and propose a novel brain-controlled framework that combines a probabilistic brain-computer interface (BCI) and a model predictive controller (MPC). First, the probabilistic BCI is developed, featuring the sigmoid fitting-filter bank canonical correlation analysis (SF-FBCCA) algorithm, which serves as the core of the BCI system by decoding EEG signals and generating brain commands along with their associated probabilities. Second, an auxiliary MPC is integrated into the probabilistic BCI system to provide decision-making assistance while preserving the users' primary brain control authority. The weights of the cost function are adaptively determined based on the command probabilities. Finally, simulation-based evaluations were conducted using the WMR in a path-keeping scenario. The results demonstrate that the proposed framework significantly improves control accuracy and efficiency compared to direct brain control approaches, reducing the average lateral error by 58.02% and the average yaw angle error by 60.06%. Additionally, the MPC employing adaptive weights further improves overall performance. These findings offer theoretical insights and technical references for future research on BCI-based control frameworks.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":9.4,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560023","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