IEEE Transactions on Systems Man Cybernetics-Systems最新文献_第10页

Smoothing and Matrix Decomposition-Based Stacked Bidirectional GRU Model for Machine Downtime Forecasting 基于平滑和矩阵分解的堆叠双向GRU模型用于机器停机时间预测

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-15 DOI: 10.1109/TSMC.2025.3582768

Waqar Ahmed Khan;Sai-Ho Chung;Shi Qiang Liu;Mahmoud Masoud;Xin Wen

{"title":"Smoothing and Matrix Decomposition-Based Stacked Bidirectional GRU Model for Machine Downtime Forecasting","authors":"Waqar Ahmed Khan;Sai-Ho Chung;Shi Qiang Liu;Mahmoud Masoud;Xin Wen","doi":"10.1109/TSMC.2025.3582768","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3582768","url":null,"abstract":"The machine downtime occurring during routine production (MDT_RP) because of recessive disturbances (RecDs) can cause huge economic losses and slow down production. In modern industries, condition monitoring, prognosis, and maintenance policies are widely applied to minimize machine failures caused by dominant disturbances (DomDs). However, MDT_RP, because of RecD, has rarely been explored. RecD multivariate time series data faces the challenge of changing information with many noisy and abnormal data points, making it difficult for sequential methods (SMs) to forecast MDT_RP accurately. To address this gap, a novel smoothing and matrix decomposition (MD) based stacked bidirectional gated recurrent unit (STMD_SBiGRU) is proposed for MDT_RP forecasting. Existing SMs have disadvantages in that they are highly affected by noisy data, which significantly affects their feature information extraction capability. The generated error gets amplified during forward propagation, thus interfering with the parameter’s optimization. The proposed STMD_SBiGRU has the advantage of capturing the maximum variance in the dataset by using various MD methods, as well as reducing abnormalities by applying various smoothing factors. This dual innovation of integrating MD and smoothing facilitates the effective distribution of parameters across multiple stacked layers and directions in a proposed model, thus avoiding complexity and overfitting problems of conventional SMs while improving network generalization performance. The extensive experimental work demonstrates that STMD_SBiGRU can forecast MDT_RP with better performance and is highly robust to noisy data compared to other data-driven methods.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7215-7227"},"PeriodicalIF":8.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100435","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

Dynamic Event-Triggered Quantized Control for Switched Systems Under DoS Attacks: A Min-Derivative Switching Strategy DoS攻击下切换系统的动态事件触发量化控制：最小导数切换策略

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-15 DOI: 10.1109/TSMC.2025.3584062

Hanqing Qu;Bo-Chao Zheng;Jiasheng Shi

{"title":"Dynamic Event-Triggered Quantized Control for Switched Systems Under DoS Attacks: A Min-Derivative Switching Strategy","authors":"Hanqing Qu;Bo-Chao Zheng;Jiasheng Shi","doi":"10.1109/TSMC.2025.3584062","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3584062","url":null,"abstract":"This article studies the <inline-formula> <tex-math>$H_{infty }$ </tex-math></inline-formula> control problem for switched systems with dynamic event-triggering and quantization schemes subject to denial-of-service (DoS) attacks. First, the resilient event-triggering and quantization schemes against DoS are developed, allowing the triggering parameter and quantization density to be dynamically adjusted. Subsequently, we introduce a time-dependent piecewise Lyapunov function that remains nonincreasing at discontinuity points. This function, along with an auxiliary functional, is dedicated to establishing criteria for the stability with <inline-formula> <tex-math>$L_{2}$ </tex-math></inline-formula> gain property of switched systems, under which the frequency of DoS attacks no longer directly impacts the exponential stability decay rate. In contrast to the general min-switching rule, the min-derivative switching strategy in this article is formulated based on the derivative of Lyapunov function and serves to make the time-dependent Lyapunov function decrease. Moreover, the switching law ensures that switches occur only at discrete sampling instants, thereby avoiding Zeno behavior. Finally, two simulation examples are provided to illustrate the feasibility and superiority of our approaches.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7425-7436"},"PeriodicalIF":8.7,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090163","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

Prospect Theory-Based Portfolio Selection Using Multiple Fuzzy Reference Intervals 基于前景理论的多模糊参考区间投资组合选择

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-14 DOI: 10.1109/TSMC.2025.3578997

Xianhe Wang;Bo Wang;Long Teng;Yaoxin Wu

引用次数: 0

Knowledge-Guided Multiview Hierarchical Evolutionary Algorithm for Flexible Job Shop Scheduling With Finite Skilled Workers 有限熟练工人柔性作业车间调度的知识引导多视图分层进化算法

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-14 DOI: 10.1109/TSMC.2025.3583207

Rui Li;Ling Wang;Hongyan Sang;Lizhong Yao

{"title":"Knowledge-Guided Multiview Hierarchical Evolutionary Algorithm for Flexible Job Shop Scheduling With Finite Skilled Workers","authors":"Rui Li;Ling Wang;Hongyan Sang;Lizhong Yao","doi":"10.1109/TSMC.2025.3583207","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3583207","url":null,"abstract":"This work addresses the flexible job shop scheduling with finite skilled workers, extending classical flexible job shop scheduling by incorporating operation decomposition, finite worker, and worker transfer. These new problem features significantly increase the complexity of solving, as several operations requiring multiple workers can lead to worker competition, causing delays in other operations that depend on the same workers. Previous studies focused on either operation decomposition or worker transfer but did not address the issue of worker competition. To tackle this challenging optimization problem, we propose a knowledge-guided hierarchical evolutionary algorithm (KHEA) with multiview cooperative neighborhood search. The key contributions of this work are as follows: 1) a hierarchical solving framework is proposed to reduce the solving difficulty. This problem is decomposed into three levels. The first level ignores the worker assignment and the second level starts optimizing it. The final level then refines the global solution; 2) a knowledge-guided crossover operator with a feedback schema is designed to improve the efficiency of crossover operations; and 3) a multiview cooperative neighborhood search strategy is proposed to reduce the idle time caused by worker competition. This involves designing a new disjunctive graph that accounts for worker competition to identify the critical path. The information from both machine-view and worker-view Gantt charts is cooperatively utilized to minimize idle time. Our method, KHEA, was tested on two benchmarks across 28 instances and 16 large-scale instances, with equal running time for comparisons. Compared to state-of-the-arts, KHEA obtains significant superiority.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7259-7272"},"PeriodicalIF":8.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100503","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

Surrogate-Assisted Differential Evolution With Search Space Tightening for High-Dimensional Expensive Optimization Problems 搜索空间收紧的高维昂贵优化问题的代理辅助差分进化

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-14 DOI: 10.1109/TSMC.2025.3582897

Rongfeng Zhou;Chongle Ren;Zhenyu Meng;Haibin Zhu

{"title":"Surrogate-Assisted Differential Evolution With Search Space Tightening for High-Dimensional Expensive Optimization Problems","authors":"Rongfeng Zhou;Chongle Ren;Zhenyu Meng;Haibin Zhu","doi":"10.1109/TSMC.2025.3582897","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3582897","url":null,"abstract":"High-dimensional expensive optimization problems (HEOPs) have posed significant challenges to current surrogate-assisted differential evolution algorithms (SADEs) because of the curse of dimensionality. To enhance the optimization efficiency and solution accuracy for HEOPs, Surrogate-assisted differential evolution with search space tightening (SADE-SS) is proposed in this article. There are three main contributions in SADE-SS: first, a novel parameter adaptation strategy is incorporated into the framework of SADE to improve its scalability by leveraging information from approximated fitness values. Second, a search space tightening strategy is proposed to strengthen the local exploitation capacity by identifying promising local search spaces. Third, a switching strategy is proposed to manage the global and local surrogate-assisted searches, aiming to balance exploration and exploitation capacities. Experiments on expensive benchmark functions with dimensions ranging from 30 to 400 were conducted to verify the effectiveness of SADE-SS for HEOPs. Moreover, ablation experiments were conducted to validate each proposed component. Comprehensive experimental results demonstrate that SADE-SS can secure highly competitive performance over state-of-the-art SAEAs for HEOPs.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7356-7368"},"PeriodicalIF":8.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145089946","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

Online Reinforcement Learning Algorithm Design for Adaptive Optimal Consensus Control Under Interval Excitation 区间激励下自适应最优一致控制的在线强化学习算法设计

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-11 DOI: 10.1109/TSMC.2025.3583212

Yong Xu;Qi-Yue Che;Meng-Ying Wan;Di Mei;Zheng-Guang Wu

{"title":"Online Reinforcement Learning Algorithm Design for Adaptive Optimal Consensus Control Under Interval Excitation","authors":"Yong Xu;Qi-Yue Che;Meng-Ying Wan;Di Mei;Zheng-Guang Wu","doi":"10.1109/TSMC.2025.3583212","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3583212","url":null,"abstract":"This article proposes online data-based reinforcement learning (RL) algorithm for adaptive output consensus control of heterogeneous multiagent systems (MASs) with unknown dynamics. First, we employ the adaptive control technique to design a distributed observer, which provides an estimation of the leader for partial agents, thereby eliminating the need for the global information. Then, we propose a novel data-based adaptive dynamic programming (ADP) approach, associated with a double-integrator operator, to develop an online data-driven learning algorithm for learning the optimal control policy. However, existing optimal control strategy learning algorithms rely on the persistent excitation conditions (PECs), the full-rank condition, and the offline storage of historical data. To address these issues, our proposed method learns the optimal control policy online by solving a data-driven linear regression equations (LREs) based on an online-verifiable interval excitation (IE) condition, instead of relying on PEC. In addition, the uniqueness of the LRE solution is established by verifying the invertibility of a matrix, instead of satisfying the full-rank condition related to PEC and historical data storage as required in existing algorithms. It is demonstrated that our proposed learning algorithm not only guarantees optimal tracking with unknown dynamics but also relaxes some of the strict conditions of existing learning algorithms. Finally, a numerical example is provided to validate the effectiveness and performance of the proposed algorithms.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7325-7334"},"PeriodicalIF":8.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090071","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

PCDT: Pessimistic Critic Decision Transformer for Offline Reinforcement Learning PCDT：离线强化学习的悲观批评决策转换器

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-11 DOI: 10.1109/TSMC.2025.3583392

Xuesong Wang;Hengrui Zhang;Jiazhi Zhang;C. L. Philip Chen;Yuhu Cheng

{"title":"PCDT: Pessimistic Critic Decision Transformer for Offline Reinforcement Learning","authors":"Xuesong Wang;Hengrui Zhang;Jiazhi Zhang;C. L. Philip Chen;Yuhu Cheng","doi":"10.1109/TSMC.2025.3583392","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3583392","url":null,"abstract":"decision transformer (DT), as a conditional sequence modeling (CSM) approach, learns the action distribution for each state using historical information, such as trajectory returns, offering a supervised learning paradigm for offline reinforcement learning (Offline RL). However, due to the fact that DT solely concentrates on an individual trajectory with high returns-to-go, it neglects the potential for constructing optimal trajectories by combining sequences of different actions. In other words, traditional DT lacks the trajectory stitching capability. To address the concern, a novel DT (PCDT) for Offline RL is proposed. Our approach begins by pretraining a standard DT to explicitly capture behavior sequences. Next, we apply the sequence importance sampling to penalize actions that significantly deviate from these behavior sequences, thereby constructing a pessimistic critic. Finally, Q-values are integrated into the policy update process, enabling the learned policy to approximate the behavior policy while favoring actions associated with the highest Q-value. Theoretical analysis shows that the sequence importance sampling in pessimistic critic decision transformer (PCDT) establishes a pessimistic lower bound, while the value optimality ensures that PCDT is capable of learning the optimal policy. Results on the D4RL benchmark tasks and ablation studies show that PCDT inherits the strengths of actor–critic (AC) and CSM methods, achieving the highest normalized scores on challenging sparse-reward and long-horizon tasks. Our code are available at <uri>https://github.com/Henry0132/PCDT</uri>.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7247-7258"},"PeriodicalIF":8.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100364","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 Fixed-Time Event-Triggered Consensus Tracking Control for Robotic Multiagent Systems 机器人多智能体系统的自适应固定时间事件触发一致性跟踪控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-11 DOI: 10.1109/TSMC.2025.3582649

Ben Niu;Xinliang Zhao;Yahui Gao;Shengtao Li;Jihang Sui;Huanqing Wang

引用次数: 0

Observer-Based DETM-Switching- H∞ Control for Disturbed Servo Systems Under DoS Attacks DoS攻击下的扰动伺服系统基于观测器的detm -切换H∞控制

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-11 DOI: 10.1109/TSMC.2025.3582912

Qiaofeng Zhang;Meng Li;Yong Chen;Meng Zhang;Haiyu Song

{"title":"Observer-Based DETM-Switching- H∞ Control for Disturbed Servo Systems Under DoS Attacks","authors":"Qiaofeng Zhang;Meng Li;Yong Chen;Meng Zhang;Haiyu Song","doi":"10.1109/TSMC.2025.3582912","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3582912","url":null,"abstract":"This article investigates the secure control of a class of servo DC motors in the presence of input–output disturbances and DoS attacks. A multichannel observer-based switching <inline-formula> <tex-math>$Hinfty $ </tex-math></inline-formula> control strategy is proposed and a dynamic event triggering mechanism (DETM) is designed to save network resources. First, a mathematical model of servo DC motor containing input–output disturbances is developed and discretized to make it more suitable for computer control. Then, a state observer and a multichannel transmission strategy based on Markov theory are designed in order to obtain the accurate knowledge of disturbed system and transmit it to the remote controller under DoS attack. Third, observer-based state feedback switching <inline-formula> <tex-math>$Hinfty $ </tex-math></inline-formula> control strategy is proposed and the stability is demonstrated. Furthermore, the DETM is presented to reduce the occupation of network resources by introducing dynamic trigger variable. Finally, the performance of the characterized control strategy is verified by a numerical simulation and a semi-physical simulation.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7315-7324"},"PeriodicalIF":8.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145090070","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

Intelligent Control in Asymmetric Decision-Making: An Event-Triggered RL Approach for Mismatched Uncertainties 非对称决策中的智能控制：一种不匹配不确定性的事件触发强化学习方法

IF 8.7 1区计算机科学

IEEE Transactions on Systems Man Cybernetics-Systems Pub Date : 2025-07-10 DOI: 10.1109/TSMC.2025.3583066

Xiangnan Zhong;Zhen Ni

{"title":"Intelligent Control in Asymmetric Decision-Making: An Event-Triggered RL Approach for Mismatched Uncertainties","authors":"Xiangnan Zhong;Zhen Ni","doi":"10.1109/TSMC.2025.3583066","DOIUrl":"https://doi.org/10.1109/TSMC.2025.3583066","url":null,"abstract":"Artificial intelligence (AI)-based multiplayer systems have attracted increasing attention across diverse fields. While most research focuses on simultaneous-move multiplayer games to achieve Nash equilibrium, there are complex applications that involve hierarchical decision-making, where certain players act before others. This power asymmetry increases the complexity of strategic interactions, especially in the presence of mismatched uncertainties that can compromise data reliability and decision-making. To this end, this article develops a novel event-triggered reinforcement learning (RL) approach for hierarchical multiplayer systems with mismatched uncertainties. Specifically, by establishing an auxiliary augment system and designing appropriate cost functions for the high-level leader and low-level followers, we reformulate the hierarchical robust control problem as an optimization task within the Stackelberg–Nash game framework. Furthermore, an event-triggered scheme is designed to reduce the computational overhead and a neural-RL-based method is developed to automatically learn the event-triggered control policies for hierarchical players. Theoretical analyses are conducted to 1) demonstrate the stability preservation of the designed robust-optimal transformation; 2) verify the achievement of Stackelberg–Nash equilibrium under the developed event-triggered policies; and 3) guarantee the boundedness of the impulsive closed-loop system. Finally, the simulation studies validate the effectiveness of the developed method.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"55 10","pages":"7288-7301"},"PeriodicalIF":8.7,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145100363","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