{"title":"Robust Consensus of Constrained AUVs With Non-Uniform Time-Varying Delays and Disturbances","authors":"Tao Yan, Zhe Xu, Simon X. Yang, S. Andrew Gadsden","doi":"10.1109/tcyb.2025.3610787","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3610787","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"15 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133796","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":"Self Learning Fuzzy Logic-Based Robust Control of Robotic Manipulators Driven With BLDC Motors: A Task Space Control Approach","authors":"Bayram Melih Yilmaz, Sukru Unver, Erman Selim, Enver Tatlicioglu, Irem Saka, Erkan Zergeroglu","doi":"10.1109/tcyb.2025.3608628","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3608628","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"28 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133800","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":"Learning-Based Fault-Tolerant Optimal Formation Control of Helicopters: An Incremental Fully Actuated System Approach","authors":"Ke Zhang, Qiyang Miao, Bin Jiang","doi":"10.1109/tcyb.2025.3610020","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3610020","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145133797","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":"HMAMRL: Multicriterion Flexible Coordinated Control for Coal-Fired Power Generation Systems under Wide Load Operation.","authors":"Xiaomin Liu,Mengjun Yu,Chunyu Yang,Haoyu Wang,Linna Zhou,Huaichun Zhou","doi":"10.1109/tcyb.2025.3610512","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3610512","url":null,"abstract":"Flexible and efficient wide-load tracking in coal-fired power generation systems (CPGSs) is crucial for integrating renewable energy. To address the challenges arising from the dynamic characteristics and task distribution differences during the wide-load operation of thermal power units, this article proposes a novel hierarchical model-agnostic meta reinforcement learning (HMAMRL) framework. This framework combines inner meta-learning for quick adaptation within task categories and outer meta-learning for sharing general task knowledge, ensuring robust generalization under different load conditions. Meanwhile, an adaptive multicriterion reward function design method is proposed to dynamically balance load tracking costs, coal consumption costs, and input fluctuation costs. Moreover, a truncated proximal policy optimization (TPPO) algorithm ensures precise load control within physical constraints. Experimental results on the 160 and 1000 MW CPGSs demonstrate the effectiveness and superiority of the proposed algorithm.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"16 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134055","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":"Model-Free Output Regulation of Networked Systems Under Unknown Hybrid Attacks.","authors":"Xiran Cui,Zheng-Guang Wu,Yi Dong,Zhong-Ping Jiang","doi":"10.1109/tcyb.2025.3608261","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3608261","url":null,"abstract":"This article considers the output regulation problem for an unknown discrete-time system subject to the random combination of denial-of-service, replay, and deception attacks on both sensor-controller and controller-actuator channels. We propose a learning-based receding-horizon control with historical output signals. It offers two advantages over state and output feedback regulators in the sense that it requires neither exact knowledge of system dynamics nor a direct measurement of external disturbance on one hand, and on the other hand, it can counteract the adverse impact of hybrid attacks on the executive capability of the actuator, regardless of the seriously tampered data on the sensor-controller channel. To overcome technical difficulties from hybrid attacks on both channels, we generalize the Markov-parameter-based time-series control method to generate a data packet containing the current and future control inputs, which are further compromised on the controller-actuator channel. Thus, a recovery procedure is additionally designed to solve the model-free output regulation problem by distinguishing the undamaged predicted inputs based on the proposed hybrid attack detection procedure.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"61 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127220","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":"Autonomous Tracked Vehicles Operating in Cluttered and Unknown Environments: A Networked Set-Theoretic Receding Horizon Control Strategy.","authors":"Valerio Scordamaglia,Alessia Ferraro,Giuseppe Franze","doi":"10.1109/tcyb.2025.3608559","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3608559","url":null,"abstract":"In this article, the constrained navigation problem for autonomous robots moving in unknown cluttered environments is considered. In particular, it is required to ensure the safety of the path planning and control units during the on-line operations. This statement gives rise to a networked control framework whose the key critical aspects are addressed by resorting to model predictive control technicalities developed within a set-theoretic approach. In particular, a novel control architecture is conceived whose the main features can be summarized as follows: anti-collision capabilities despite time-induced time-delay occurrences along the communication medium; mission accomplishment despite unpredictable obstacle occurrences along the nominal path. These properties are formally proven together with ultimate uniformly boundedness and constraints fulfillment of the regulated trajectory regardless of the vehicle uncertainties. In particular, SSTMR are considered for their flexibility and adaptability to operate in arduous scenarios for hazard missions. Final experiments are provided to show the effectiveness and to highlight the main advantages of the proposed control architecture.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"40 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127219","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}
Ke Zou,Yidi Chen,Ling Huang,Nan Zhou,Xuedong Yuan,Xiaojing Shen,Meng Wang,Rick Siow Mong Goh,Yong Liu,Yih Chung Tham,Huazhu Fu
{"title":"Toward Reliable Medical Image Segmentation by Modeling Evidential Calibrated Uncertainty.","authors":"Ke Zou,Yidi Chen,Ling Huang,Nan Zhou,Xuedong Yuan,Xiaojing Shen,Meng Wang,Rick Siow Mong Goh,Yong Liu,Yih Chung Tham,Huazhu Fu","doi":"10.1109/tcyb.2025.3604432","DOIUrl":"https://doi.org/10.1109/tcyb.2025.3604432","url":null,"abstract":"Medical image segmentation is critical for disease diagnosis and treatment assessment. However, concerns regarding the reliability of segmentation regions persist among clinicians, mainly attributed to the absence of confidence assessment, robustness, and calibration to accuracy. To address this, we introduce deep evidential segmentation model (DEviS), an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. DEviS not only enhances the calibration and robustness of baseline segmentation accuracy but also provides high-efficiency uncertainty estimation for reliable predictions. By leveraging SL theory, we explicitly model probability and uncertainty for medical image segmentation. Here, the Dirichlet distribution parameterizes the distribution of probabilities for different classes of the segmentation results. To generate calibrated predictions and uncertainty, we develop a trainable CUP. Furthermore, DEviS incorporates an uncertainty-aware filtering (UAF) module, which designs the metric of uncertainty-calibrated error to filter out-of-distribution (OOD) data. We conducted validation studies on publicly available datasets, including ISIC2018, KiTS2021, LiTS2017, and BraTS2019, to assess the accuracy and robustness of different backbone segmentation models enhanced by DEviS, as well as the efficiency and reliability of uncertainty estimation. Additionally, two potential clinical trials were conducted using the UAF module. The clinical application conducted on the Johns Hopkins OCT and Duke OCT-DME datasets demonstrated the effectiveness of the model in filtering OOD data. The second trial evaluated its efficacy in filtering high-quality data on the FIVES datasets. At last, the proposed DEviS method was extended to semi-supervised medical image segmentation, where it exhibited strong robustness under noisy conditions. Our code has been released in https://github.com/Cocofeat/DEviS.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"80 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145127221","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}