IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第9页

IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-18 DOI: 10.1109/TSMC.2025.3539409

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IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-18 DOI: 10.1109/TSMC.2025.3539405

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IEEE Systems, Man, and Cybernetics Society Information

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-18 DOI: 10.1109/TSMC.2025.3539415

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TechRxiv: Share Your Preprint Research With the World!

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-18 DOI: 10.1109/TSMC.2025.3539411

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IEEE Transactions on Systems, Man, and Cybernetics: Systems Publication Information

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-18 DOI: 10.1109/TSMC.2025.3539413

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An Adaptive p-Norms-Based Kinematic Calibration Model for Industrial Robot Positioning Accuracy Promotion

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-17 DOI: 10.1109/TSMC.2025.3535783

Tinghui Chen;Weiyi Yang;Shuai Li;Xin Luo

{"title":"An Adaptive p-Norms-Based Kinematic Calibration Model for Industrial Robot Positioning Accuracy Promotion","authors":"Tinghui Chen;Weiyi Yang;Shuai Li;Xin Luo","doi":"10.1109/TSMC.2025.3535783","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3535783","url":null,"abstract":"Industrial robots inevitably incur kinematic errors in the advanced manufacturing and assembly processes, resulting in the severe reduction of the absolute positioning accuracy (APA). Kinematic calibration (KC) is well-known as a vital technique in APA-promoting tasks. However, existing KC models generally adopt a single distance-oriented Loss, e.g., an <inline-formula> <tex-math>$L_{2}$ </tex-math></inline-formula> norm-oriented one that neglects the featured <inline-formula> <tex-math>$L_{p}$ </tex-math></inline-formula> norms. In response to this critical issue, this study presents an Adaptive p-norms-oriented Kinematic Calibration (ApKC) model on the basis of threefold ideas: 1) studying the effects of diversified <inline-formula> <tex-math>$L_{p}$ </tex-math></inline-formula> norms on the industrial robot calibration performance; 2) combining multiple <inline-formula> <tex-math>$L_{p}$ </tex-math></inline-formula> norms to obtain the aggregated loss with the hybrid effects by different norms; and 3) implementing the weight adaptation on the norm components of the aggregated loss, and rigorously prove its ensemble capability benefiting the calibration performance. Afterwards, a novel Newton interpolated Adaptive Differential Evolution (NADE) algorithm is further proposed to optimize the ApKC model. Empirical studies on an HRS JR680 industrial robot demonstrate that the achieved ApKC-NADE calibrator can significantly reduce the robot’s maximum positioning error from 4.610 to 0.856 mm. It can vigorously support the high-accuracy application of industrial robots.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 4","pages":"2937-2949"},"PeriodicalIF":8.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667577","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}

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A Unified Sparse Training Framework for Lightweight Lifelong Deep Reinforcement Learning

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-17 DOI: 10.1109/TSMC.2025.3537990

Meng Xu;Xinhong Chen;Weiwei Fu;Yi-Rong Lin;Yung-Hui Li;Jianping Wang

{"title":"A Unified Sparse Training Framework for Lightweight Lifelong Deep Reinforcement Learning","authors":"Meng Xu;Xinhong Chen;Weiwei Fu;Yi-Rong Lin;Yung-Hui Li;Jianping Wang","doi":"10.1109/TSMC.2025.3537990","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3537990","url":null,"abstract":"Lifelong deep reinforcement learning (DRL) methods enable continuous adaptation to new tasks and retention of old knowledge. However, these methods often necessitate large model sizes, leading to substantial computational and storage resource requirements during training and inference. Unfortunately, existing research has not yet provided a lightweight solution to address this issue. This work aims to develop a generic method that can be seamlessly integrated into existing lifelong DRL methods to facilitate their achievement of lightweight models while also yielding higher returns. While sparse training (ST) methods have been extensively used in the DRL community to achieve lightweight models, they exacerbate the issue of catastrophic forgetting and compromise generalization when applied in lifelong DRL. To improve generalization, we develop a gradient optimization method that leverages sharpness-aware minimization (SAM) to smooth the gradient surface of the model without introducing excessive computational complexity. In addition, to alleviate catastrophic forgetting and promote model convergence, we introduce a priority-based approach that samples effective past experiences from the replay buffer. Extensive experiments demonstrate that our approach achieves 90% sparsity in five representative lifelong DRL methods while achieving higher episode return and average return (up to 34% improvement) across all episodes compared to the dense models.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 4","pages":"2998-3012"},"PeriodicalIF":8.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667724","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

Stability Analysis of Networked Stochastic Systems With Time Delays Under Deception Attacks by Sampled-Data Control

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-14 DOI: 10.1109/TSMC.2025.3536018

Peiyang Lin;Feiqi Deng;Xueyan Zhao;Fangzhe Wan;Yongjia Huangs

{"title":"Stability Analysis of Networked Stochastic Systems With Time Delays Under Deception Attacks by Sampled-Data Control","authors":"Peiyang Lin;Feiqi Deng;Xueyan Zhao;Fangzhe Wan;Yongjia Huangs","doi":"10.1109/TSMC.2025.3536018","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3536018","url":null,"abstract":"This article focuses on the mean-square exponential stability of networked stochastic systems with time delays (NSSTDs) under nonlinear coupling and deception attacks, employing a sampled-data control strategy. A generalized Halanay inequality for NSSTDs is proposed to investigate the stability of the closed-loop system, where multiple time delays with different bounds are considered, with an incorporation of the graph theory. By the comparison of the continuous control system and the sample-data control system, the equivalence condition on the stabilities of the two systems is studied. Meanwhile, estimates for the maximum tolerable attack probability and the corresponding largest sampling period are obtained. Moreover, a qualitative analysis of various indicators for the deception attacks and the sampling period is revealed. Following this, the theorized results are applied to linear systems with multiple time delays under nonlinear coupling, and matrix inequalities for identifying the appropriate value of the control gain are provided by the generalized Halanay inequality. To show the correctness of the results, computational simulations are conducted.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 4","pages":"2950-2960"},"PeriodicalIF":8.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667578","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 Dynamic Programming for PMSM Control Under Safety, Robustness, and Optimality Constraints 安全、鲁棒性和最优性约束下 PMSM 控制的自适应动态编程

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-13 DOI: 10.1109/TSMC.2024.3510544

Zhong-Xin Fan;Shihua Li;Jinya Su

{"title":"Adaptive Dynamic Programming for PMSM Control Under Safety, Robustness, and Optimality Constraints","authors":"Zhong-Xin Fan;Shihua Li;Jinya Su","doi":"10.1109/TSMC.2024.3510544","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3510544","url":null,"abstract":"This article aims to derive an adaptive optimal speed regulator for permanent magnet synchronous motors (PMSMs) affected by both disturbances and actuator faults. Load torque is first modeled as a mismatched disturbance, where its estimation via a disturbance observer is drawn to construct an error system. Then, optimal speed regulation problem for PMSM is equivalently transformed into an optimal control problem for the error system. Given the presence of model variable couplings, an adaptive dynamic programming method is adopted to derive the optimal controller, where a critic neural network (NN) and an actor NN are used to approximate the cost function and the optimal controller, respectively. Notably, this article addresses the simultaneous occurrence of disturbances and actuator faults within the optimal control framework by designing separate treatments. Eventually, a composite controller, fulfilling optimality, robustness and safety constraints, is presented with rigorous proof via Lyapunov method. The proposed method is substantiated through both comparative numerical examples and experimental validation on a PMSM platform.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 4","pages":"2724-2733"},"PeriodicalIF":8.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667739","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}

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A Novel Modeling Approach for Cumulative Belief Rule-Base With Joint Optimization and Rule Synthesis

IF 8.6 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-02-11 DOI: 10.1109/TSMC.2025.3534988

Long-Hao Yang;Dan-Ning Yu;Fei-Fei Ye;Haibo Hu;Qingqing Ye

{"title":"A Novel Modeling Approach for Cumulative Belief Rule-Base With Joint Optimization and Rule Synthesis","authors":"Long-Hao Yang;Dan-Ning Yu;Fei-Fei Ye;Haibo Hu;Qingqing Ye","doi":"10.1109/TSMC.2025.3534988","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3534988","url":null,"abstract":"Cumulative belief rule-based system (CBRBS) is a recent representative of explainable artificial intelligence (XAI). However, the use of CBRBS as XAI still faces many challenges, e.g., over-reliance on expert experience and applying unreasonable rule synthesis in the existing modeling process. Hence, a novel modeling approach is proposed for constructing CBRBS in the aim of providing a better XAI, in which a joint optimization model is proposed first to describe the mathematical model of parameter and structure optimization, and the corresponding algorithm is further designed to automatically achieve the joint optimization of CBRBS. Afterward, a domain-based calculation method of synthesis factor is proposed to develop a new rule synthesis method for CBRBS, which not only achieves the reduction of inefficient and inconsistent rules but also takes into account interpretability and generalization ability. In experimental analysis, the proposed modeling approach is employed to construct CBRBS for handling rice taste assessment and benchmark classification problems. The comparison results show that the proposed approach makes it possible for CBRBS to achieve a good balance between model complexity and inference accuracy. More importantly, the resulting CBRBS has better accuracy and lower complexity than some existing rule-based systems and classical classifiers.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 4","pages":"2961-2973"},"PeriodicalIF":8.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667486","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