IEEE Transactions on Systems Man Cybernetics-Systems最新文献

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-10-16 DOI: 10.1109/TSMC.2025.3618075

引用次数: 0

IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors IEEE系统、人与控制论汇刊：作者的系统信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-10-16 DOI: 10.1109/TSMC.2025.3618077

引用次数: 0

IEEE Transactions on Systems, Man, and Cybernetics: Systems Information for Authors IEEE系统、人与控制论汇刊：作者的系统信息

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-10-16 DOI: 10.1109/TSMC.2025.3618069

引用次数: 0

Constrained Sampling-Based MPC Using Path Integral for Collision-Free Robot Manipulation 基于路径积分的约束采样MPC无碰撞机器人操作

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-26 DOI: 10.1109/TSMC.2025.3611922

Xingfang Wang;Hui Li;Dong Wang;Xiao Huang;Zhihong Jiang

{"title":"Constrained Sampling-Based MPC Using Path Integral for Collision-Free Robot Manipulation","authors":"Xingfang Wang;Hui Li;Dong Wang;Xiao Huang;Zhihong Jiang","doi":"10.1109/TSMC.2025.3611922","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611922","url":null,"abstract":"The dynamic and unknown human behaviors in human–robot interaction make it challenging for collision-free robot manipulation. Although sampling-based model predictive control (MPC) has achieved real-time control in the above scenarios, it is hard to handle equality hard constraints, such as working along a specified trajectory, due to sampling disturbances. To improve manipulation performance under multiple constraints, this article presents a novel constrained sampling-based MPC (CSMPC) method using path integral. First, hierarchical optimization combining policy sampling projection and the Lagrange multiplier method is used to handle equality hard constraints for high-precision manipulation tasks. Second, collision avoidance and smooth motion are modeled as inequality soft constraints, where collision detection and time series prediction are used to ensure the safety and smoothness of dynamic interaction. Finally, an adaptive noise method is built to improve the stability of physical robot manipulation. The simulation and experiment results demonstrate that the proposed method enables a 7-DOF robot manipulator to achieve precise manipulation while avoiding dynamic obstacles.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8701-8714"},"PeriodicalIF":8.7,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335306","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

Graph-Based Dual-Agent Deep Reinforcement Learning for Dynamic Human–Machine Hybrid Reconfiguration Manufacturing Scheduling 基于图的双智能体深度强化学习的动态人机混合重构制造调度

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-26 DOI: 10.1109/TSMC.2025.3612300

Yuxin Li;Qihao Liu;Chunjiang Zhang;Xinyu Li;Liang Gao

{"title":"Graph-Based Dual-Agent Deep Reinforcement Learning for Dynamic Human–Machine Hybrid Reconfiguration Manufacturing Scheduling","authors":"Yuxin Li;Qihao Liu;Chunjiang Zhang;Xinyu Li;Liang Gao","doi":"10.1109/TSMC.2025.3612300","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3612300","url":null,"abstract":"Human–machine hybrid reconfiguration manufacturing is an emerging paradigm in the field of precision equipment production and can greatly improve the production capability of the workshop. However, numerous complex constraints and a dynamic environment make reasonable scheduling very difficult. To this end, this article studies the dynamic human–machine hybrid reconfiguration manufacturing scheduling problem (DHMRSP) and proposes a novel deep reinforcement learning (DRL) scheduling method. Specifically, a dual-agent Markov decision process (MDP) is established, which can handle seven complex constraints and three disturbance events. Then, a heterogeneous competition graph attention network (HCGAN) is designed, where the meta-path-based subgraph conversion reflects the resource-operation competition, and three modules use node-level attention and semantic-level attention to realize important information embedding. Afterward, a dual proximal policy optimization (PPO) algorithm with HCGAN and mixed action space (HM-DPPO) is proposed, where the allocation agent and reconfiguration agent achieve collaborative learning by taking joint action and sharing graph embeddings and reward. Experimental results prove that the proposed approach outperforms rules, genetic programming (GP), and three DRL methods on different instances and can effectively handle various disturbance events.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8729-8741"},"PeriodicalIF":8.7,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335326","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

Global Regulation of Time-Varying Stochastic Nonlinear Systems via Output Feedback and Its Application in One-Link Manipulator 时变随机非线性系统的输出反馈全局调节及其在单连杆机械臂中的应用

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-26 DOI: 10.1109/TSMC.2025.3611915

Xian-Long Yin;Zong-Yao Sun;Changyun Wen;Chih-Chiang Chen

引用次数: 0

Polynomial-Based Gain-Scheduling Mechanism of Fuzzy Markov Jump System With Incomplete Transition Probability Information With Experimental Validation 不完全转移概率模糊马尔可夫跳跃系统基于多项式的增益调度机制及实验验证

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-24 DOI: 10.1109/TSMC.2025.3611824

Xingchen Shao;Lipo Mo;Xiangpeng Xie

{"title":"Polynomial-Based Gain-Scheduling Mechanism of Fuzzy Markov Jump System With Incomplete Transition Probability Information With Experimental Validation","authors":"Xingchen Shao;Lipo Mo;Xiangpeng Xie","doi":"10.1109/TSMC.2025.3611824","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611824","url":null,"abstract":"This article investigates the stabilization problem of fuzzy Markov jump systems (F-MJSs) with incomplete transition probability (TP) information. Existing methods for handling partially unknown TPs often introduce excessive conservatism using scaling techniques that may violate the fundamental stochastic constraints. To address this issue, we propose a novel polynomial-based gain-scheduling control framework that integrates a polytopic probability reconstruction strategy. This strategy rigorously preserves the stochastic completeness of TP matrices (TPMs) while reducing conservatism in controller design. By leveraging homogeneous polynomial theory, we further establish a codesign methodology for both polynomial Lyapunov functions and fuzzy controllers, significantly expanding the feasible solution space. Theoretical analysis demonstrates that the proposed method achieves substantially reduced conservatism compared with conventional aggregated approximation approaches. Numerical simulations reveal the improvement compared with classical aggregated treatment approaches. Hardware-in-the-loop (HIL) experiments on active suspension systems validate the effectiveness and robustness of the designed control strategy, especially <inline-formula> <tex-math>$gamma _{mathrm { min}}$ </tex-math></inline-formula> achieved a reduction optimization of 87.5%.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8742-8754"},"PeriodicalIF":8.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335257","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

Event-Triggered Control for Linear Positive Discrete-Time Singular Systems With Time Delay 线性正离散时滞奇异系统的事件触发控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-24 DOI: 10.1109/TSMC.2025.3608211

Nguyen Huu Sau;Mai Viet Thuan

{"title":"Event-Triggered Control for Linear Positive Discrete-Time Singular Systems With Time Delay","authors":"Nguyen Huu Sau;Mai Viet Thuan","doi":"10.1109/TSMC.2025.3608211","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3608211","url":null,"abstract":"This study addresses the stabilization of discrete-time singular systems with delays by employing an event-triggered control (ETC) method. In particular, we propose an innovative triggering mechanism that compares the measurement-error coordinates with the system state coordinates, thereby preserving system positivity even under time delays. A novel algorithm is introduced, which relies on comparing the coordinates of measurement errors and system states to maintain the positivity of the system. This article establishes sufficient conditions to ensure that the closed-loop system remains regular, causal, positive, and exponentially stable, building upon this newly formulated triggering approach and leveraging advanced matrix properties such as nonnegative matrices. To illustrate the efficacy and nontrivial nature of these conditions, we provide an algorithmic diagram and a diverse set of examples, including both simulations and a practical case study. The ETC mechanism, characterized by the sequence of event occurrences, demonstrates substantial nontrivial properties. These conditions are easily verifiable using MATLAB tools. This article also includes a range of examples, featuring both numerical simulations and a practical case study, to validate the effectiveness of the theoretical findings.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8625-8637"},"PeriodicalIF":8.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335280","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

Contrastive-Learning-Based Decision Making for Dynamic Time-Linkage Optimization 基于对比学习的动态时间链优化决策

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-24 DOI: 10.1109/TSMC.2025.3611797

Xiao-Fang Liu;Meng Gao;Yongchun Fang;Zhi-Hui Zhan;Jun Zhang

{"title":"Contrastive-Learning-Based Decision Making for Dynamic Time-Linkage Optimization","authors":"Xiao-Fang Liu;Meng Gao;Yongchun Fang;Zhi-Hui Zhan;Jun Zhang","doi":"10.1109/TSMC.2025.3611797","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611797","url":null,"abstract":"In dynamic time-linkage optimization, current decisions influence the future state of environments. To make good decisions that have a positive impact on future states, existing methods usually build a model to predict the future rewards of solutions for decision making. However, these prediction models present low accuracy since decision data are not enough to train such a complex model. To address this issue, this article proposes a contrastive-learning-based decision making (CLDM) method, which builds a contrastive model to learn the relationship between solutions but not absolute rewards and adopts a quick decision strategy to select solutions. In CLDM, a clustering-based time-linkage detection (CD) strategy is developed to measure the intensity of the time linkage, which determines whether to make decisions based on future rewards. To represent the relative relationship between solutions, a large number of contrastive samples are constructed using the limited historical decisions. A contrastive model is trained for solution comparison in terms of the combination of current fitness and future rewards. Candidate solutions are clustered into multiple groups to filter poor ones, and a few solutions are preserved to rank using the contrastive model. The winner is taken as the decision solution. Integrating CLDM into particle swarm optimization (PSO), a new algorithm named contrastive-learning-based PSO (CL-PSO) is put forward. Experimental results on multiple dynamic time-linkage optimization instances demonstrate that CL-PSO outperforms state-of-the-art algorithms in terms of solution quality. CL-PSO can also well solve the mobile robot path planning problem.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8661-8674"},"PeriodicalIF":8.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335292","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

DreamArrangement: Learning Language-Conditioned Robotic Rearrangement of Objects via Denoising Diffusion and VLM Planner 梦境排列：基于去噪扩散和VLM规划的语言条件机器人物体重排学习

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-09-24 DOI: 10.1109/TSMC.2025.3611698

Wenkai Chen;Changming Xiao;Ge Gao;Fuchun Sun;Changshui Zhang;Jianwei Zhang

{"title":"DreamArrangement: Learning Language-Conditioned Robotic Rearrangement of Objects via Denoising Diffusion and VLM Planner","authors":"Wenkai Chen;Changming Xiao;Ge Gao;Fuchun Sun;Changshui Zhang;Jianwei Zhang","doi":"10.1109/TSMC.2025.3611698","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3611698","url":null,"abstract":"The capability for robotic systems to rearrange objects based on human instructions represents a critical step toward realizing embodied intelligence. Recently, diffusion-based learning has shown significant advancements in the field of data generation while prompt-based learning has proven effective in formulating robot manipulation strategies. However, prior solutions for robotic rearrangement have overlooked the significance of integrating human preferences and optimizing for rearrangement efficiency. Additionally, traditional prompt-based approaches struggle with complex, semantically meaningful rearrangement tasks without predefined target states for objects. To address these challenges, our work first introduces a comprehensive two dimensional (2-D) tabletop rearrangement dataset, utilizing a physical simulator to capture interobject relationships and semantic configurations. Then, we present DreamArrangement, a novel language-conditioned object rearrangement scheme, consisting of two primary processes: employing a transformer-based multimodal denoising diffusion model to envisage the desired arrangement of objects, and leveraging a vision–language foundational model to derive actionable policies from text, alongside initial and target visual information. In particular, we introduce an efficiency-oriented learning strategy to minimize the average motion distance of objects. Given few-shot instruction examples, the learned policy from our synthetic dataset can be transferred to the real world without extra human intervention. Extensive simulations validate DreamArrangement’s superior rearrangement quality and efficiency. Moreover, real-world robotic experiments confirm that our method can adeptly execute a range of challenging, language-conditioned, and long-horizon tasks with a singular model. The demonstration video can be found at <uri>https://youtu.be/fq25-DjrbQE</uri>","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 11","pages":"8675-8688"},"PeriodicalIF":8.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145335318","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