A Dynamic Job-shop Scheduling Problem (DJSP) in 3C (i.e., Computer, Communication, and Consumer Electronics) manufacturing requires efficient resource allocation under dynamically changing production conditions where jobs arrive unpredictably. Traditional optimization methods struggle to provide scalable solutions due to the high computational cost of searching for the optimal schedules in large and complex environments. To address this challenge, this work proposes a Dual Graph convolutional networks-driven Dynamic Cooperative Hunting Optimizer (DG-DCHO). It integrates Graph Convolutional Networks (GCN) with metaheuristic optimization to generate high-quality schedules and significantly improve computational efficiency. A GCN generator processes graph representations of job-shop environment, captures complex dependencies among jobs and machines, and constructs high-quality initial schedules for the optimization process. A GCN evaluator estimates makespan values directly from schedule representations and replaces costly fitness evaluation, thereby minimizing computational overhead and improving optimization speed. A Dynamic Cooperative Hunting Optimizer serves as a base optimizer and generates scheduling solutions by balancing global exploration with local exploitation through an adaptive search strategy. Experimental results across various DJSP instances demonstrate that DG-DCHO consistently outperforms advanced scheduling algorithms by producing higher-quality solutions with reduced computational resources, establishing it as a scalable and effective framework for real-time dynamic scheduling of large-scale manufacturing systems. Note to Practitioners—This paper is motivated by the practical need to rapidly generate efficient production schedules for complex job shops. We propose a novel automated approach, DG-DCHO, which uses deep learning to learn the dependencies of the production environment and rapidly generate high-quality initial schedules. DG-DCHO also estimates schedule performance without relying on lengthy simulations, accelerating the optimization process with an adaptive algorithm. To apply this approach, practitioners would provide standard manufacturing data, including the sequence of operations required for each job, the constraints between operations, the list of available machines, the potential machine assignments for each operation, and the processing times. The system uses this information to automatically build the required graph model, where DG-DCHO optimizes and outputs the best scheduling sequence. This results in the faster generation of more efficient production schedules, improving responsiveness and productivity. Although the simulation results are strong, practical implementation requires integration with factory systems and initial training in artificial intelligence models. Our future plans to extend the proposed approach to addressing other dynamic optimization challenges in logistics, intelligent manufacturing,
{"title":"Dual-GNN-Driven Cooperative Optimization for Makespan-Minimized and Large-Scale 3C Dynamic Job-Shop Scheduling","authors":"Jing Bi;Chen Wang;Ziqi Wang;Junqi Zhang;Haitao Yuan;Jia Zhang;Rajkumar Buyya","doi":"10.1109/TASE.2026.3661178","DOIUrl":"10.1109/TASE.2026.3661178","url":null,"abstract":"A Dynamic Job-shop Scheduling Problem (DJSP) in 3C (i.e., Computer, Communication, and Consumer Electronics) manufacturing requires efficient resource allocation under dynamically changing production conditions where jobs arrive unpredictably. Traditional optimization methods struggle to provide scalable solutions due to the high computational cost of searching for the optimal schedules in large and complex environments. To address this challenge, this work proposes a Dual Graph convolutional networks-driven Dynamic Cooperative Hunting Optimizer (DG-DCHO). It integrates Graph Convolutional Networks (GCN) with metaheuristic optimization to generate high-quality schedules and significantly improve computational efficiency. A GCN generator processes graph representations of job-shop environment, captures complex dependencies among jobs and machines, and constructs high-quality initial schedules for the optimization process. A GCN evaluator estimates makespan values directly from schedule representations and replaces costly fitness evaluation, thereby minimizing computational overhead and improving optimization speed. A Dynamic Cooperative Hunting Optimizer serves as a base optimizer and generates scheduling solutions by balancing global exploration with local exploitation through an adaptive search strategy. Experimental results across various DJSP instances demonstrate that DG-DCHO consistently outperforms advanced scheduling algorithms by producing higher-quality solutions with reduced computational resources, establishing it as a scalable and effective framework for real-time dynamic scheduling of large-scale manufacturing systems. Note to Practitioners—This paper is motivated by the practical need to rapidly generate efficient production schedules for complex job shops. We propose a novel automated approach, DG-DCHO, which uses deep learning to learn the dependencies of the production environment and rapidly generate high-quality initial schedules. DG-DCHO also estimates schedule performance without relying on lengthy simulations, accelerating the optimization process with an adaptive algorithm. To apply this approach, practitioners would provide standard manufacturing data, including the sequence of operations required for each job, the constraints between operations, the list of available machines, the potential machine assignments for each operation, and the processing times. The system uses this information to automatically build the required graph model, where DG-DCHO optimizes and outputs the best scheduling sequence. This results in the faster generation of more efficient production schedules, improving responsiveness and productivity. Although the simulation results are strong, practical implementation requires integration with factory systems and initial training in artificial intelligence models. Our future plans to extend the proposed approach to addressing other dynamic optimization challenges in logistics, intelligent manufacturing,","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"4350-4364"},"PeriodicalIF":6.4,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146134383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Elevating Interpretability in Bearing Fault Diagnosis: A Knowledge Distillation Framework Integrating Dynamic and Causal A Priori","authors":"Xu Ding, Zihua Yan, Hao Wu, Qile Ren, Hua Zhai, Juan Xu","doi":"10.1109/tase.2026.3660370","DOIUrl":"https://doi.org/10.1109/tase.2026.3660370","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"17 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Data-Driven Event-Triggered H ∞ Load Frequency Control with Security Against DoS Attacks","authors":"Yuhao Chen, Huarong Zhao, Longquan Ma, Qiang Yang, Hongnian Yu, Li Peng","doi":"10.1109/tase.2026.3660419","DOIUrl":"https://doi.org/10.1109/tase.2026.3660419","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"147 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1109/TASE.2026.3659947
Yanmin Wu;Wei Qian;Zidong Wang
This paper is concerned with the adaptive dynamic event-triggered (DET) $H_{infty }$ control for a class of networked nonlinear systems with non-uniform sampling under the Takagi-Sugeno (T-S) fuzzy model. To address the problems of high energy consumption in sensors and the high occupancy rate of limited bandwidth, an improved adaptive DET communication mechanism with non-uniform sampling is proposed, in which the interval dynamic variable is constructed with an adaptive law. Unlike existing works, an online iterative asynchronous premise reconstruction (APR) technique is devised to tackle the challenges caused by the mismatch of premise variables. Based on the adaptive DET mechanism and the online iterative APR method, switched-like fuzzy controllers are designed, which can be switched at different triggering instances. Furthermore, to ensure stability and achieve the desired control performance, new triggering instants-dependent Lyapunov functions are constructed in accordance with the idea of event interval partitioning. Finally, the practicability of the proposed strategy is demonstrated using two practical examples. Note to Practitioners—The motivation of this study is to devise a DET $H_{infty }$ control strategy for networked T-S fuzzy systems, which can be extensively utilized in suspension systems, power systems, and permanent magnet synchronous motor systems. Considering the energy consumption of sensors and the limited bandwidth of practical communication networks, the adaptive DET communication mechanism with aperiodic sampling is proposed for the purpose of reducing the sampling frequency of sensors and unnecessary data transmission. The premise variable asynchronous challenge caused by the DET strategy, the online APR technique is designed. By using the online APR approach, the switched-like fuzzy controllers are devised, which can ensure the closed-loop systems satisfy asymptotic stability. In addition, the new triggering instants-dependent Lyapunov function is constructed to achieve desired control performance.
{"title":"Adaptive Dynamic Event-Triggered H∞ Control for Fuzzy Systems Through an Online Iterative Asynchronous Premise Reconstruction Strategy","authors":"Yanmin Wu;Wei Qian;Zidong Wang","doi":"10.1109/TASE.2026.3659947","DOIUrl":"10.1109/TASE.2026.3659947","url":null,"abstract":"This paper is concerned with the adaptive dynamic event-triggered (DET) <inline-formula> <tex-math>$H_{infty }$ </tex-math></inline-formula> control for a class of networked nonlinear systems with non-uniform sampling under the Takagi-Sugeno (T-S) fuzzy model. To address the problems of high energy consumption in sensors and the high occupancy rate of limited bandwidth, an improved adaptive DET communication mechanism with non-uniform sampling is proposed, in which the interval dynamic variable is constructed with an adaptive law. Unlike existing works, an online iterative asynchronous premise reconstruction (APR) technique is devised to tackle the challenges caused by the mismatch of premise variables. Based on the adaptive DET mechanism and the online iterative APR method, switched-like fuzzy controllers are designed, which can be switched at different triggering instances. Furthermore, to ensure stability and achieve the desired control performance, new triggering instants-dependent Lyapunov functions are constructed in accordance with the idea of event interval partitioning. Finally, the practicability of the proposed strategy is demonstrated using two practical examples. Note to Practitioners—The motivation of this study is to devise a DET <inline-formula> <tex-math>$H_{infty }$ </tex-math></inline-formula> control strategy for networked T-S fuzzy systems, which can be extensively utilized in suspension systems, power systems, and permanent magnet synchronous motor systems. Considering the energy consumption of sensors and the limited bandwidth of practical communication networks, the adaptive DET communication mechanism with aperiodic sampling is proposed for the purpose of reducing the sampling frequency of sensors and unnecessary data transmission. The premise variable asynchronous challenge caused by the DET strategy, the online APR technique is designed. By using the online APR approach, the switched-like fuzzy controllers are devised, which can ensure the closed-loop systems satisfy asymptotic stability. In addition, the new triggering instants-dependent Lyapunov function is constructed to achieve desired control performance.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"4301-4313"},"PeriodicalIF":6.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1109/TASE.2026.3660881
Yanpeng Shi;Jiangping Hu;Baogen Song;Peng Li;Lei Shi
This paper investigates the output containment tracking problem in general heterogeneous multi-agent systems facing prescribed performance and intermittent communication. In this case, a novel non-periodic intermittent control framework is introduced to facilitate the intricate nature of the complex network to achieve the containment objective. First, an intermittent communication network is built by introducing a novel intermittent interval condition combining average dwell-time and extreme value theories into the directed graph. Second, a distributed non-periodic intermittent containment control strategy is designed, utilizing an internal system and a modified containment control approach. Subsequently, a distributed prescribed performance hybrid controller is developed to achieve output containment tracking. Additionally, sufficient conditions for the exponential stability are obtained based on the non-periodic intermittent and prescribed performance control methods. This criterion adopts the characterization of the average time interval. The effectiveness of the designed hybrid control strategy is verified by the simulation example, showcasing its advantage to solve the challenges in intermittent communication and prescribed performance. Note to Practitioners—In a complicated heterogeneous network, large-scale agents involved with different dynamics need to employ distributed communication and collaborate with each other to achieve certain tracking tasks. In practical applications, the risk of both communication barriers and limited resources increases dramatically, then an effective non-periodic intermittent control scheme is proposed to guarantee the safe communication for the heterogeneous connected agents. Besides, in harsh environments, the considered agents have to work in a restricted space avoiding the hazardous area, which may limit the position or other performances. Then, it is crucial to address these issues. By introducing the modified containment control, this paper proposes a distributed prescribed performance containment control to ensure the desired transient and steady-state performances, which have important engineering significance.
{"title":"Prescribed Performance Output Containment of Heterogeneous Multi-Agent Systems With Non-Periodic Intermittent Communication","authors":"Yanpeng Shi;Jiangping Hu;Baogen Song;Peng Li;Lei Shi","doi":"10.1109/TASE.2026.3660881","DOIUrl":"10.1109/TASE.2026.3660881","url":null,"abstract":"This paper investigates the output containment tracking problem in general heterogeneous multi-agent systems facing prescribed performance and intermittent communication. In this case, a novel non-periodic intermittent control framework is introduced to facilitate the intricate nature of the complex network to achieve the containment objective. First, an intermittent communication network is built by introducing a novel intermittent interval condition combining average dwell-time and extreme value theories into the directed graph. Second, a distributed non-periodic intermittent containment control strategy is designed, utilizing an internal system and a modified containment control approach. Subsequently, a distributed prescribed performance hybrid controller is developed to achieve output containment tracking. Additionally, sufficient conditions for the exponential stability are obtained based on the non-periodic intermittent and prescribed performance control methods. This criterion adopts the characterization of the average time interval. The effectiveness of the designed hybrid control strategy is verified by the simulation example, showcasing its advantage to solve the challenges in intermittent communication and prescribed performance. Note to Practitioners—In a complicated heterogeneous network, large-scale agents involved with different dynamics need to employ distributed communication and collaborate with each other to achieve certain tracking tasks. In practical applications, the risk of both communication barriers and limited resources increases dramatically, then an effective non-periodic intermittent control scheme is proposed to guarantee the safe communication for the heterogeneous connected agents. Besides, in harsh environments, the considered agents have to work in a restricted space avoiding the hazardous area, which may limit the position or other performances. Then, it is crucial to address these issues. By introducing the modified containment control, this paper proposes a distributed prescribed performance containment control to ensure the desired transient and steady-state performances, which have important engineering significance.","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"23 ","pages":"4329-4340"},"PeriodicalIF":6.4,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1109/tase.2026.3660830
Davood Soleymanzadeh, Ivan Lopez-Sanchez, Hao Su, Yunzhu Li, Xiao Liang, Minghui Zheng
{"title":"Towards Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities","authors":"Davood Soleymanzadeh, Ivan Lopez-Sanchez, Hao Su, Yunzhu Li, Xiao Liang, Minghui Zheng","doi":"10.1109/tase.2026.3660830","DOIUrl":"https://doi.org/10.1109/tase.2026.3660830","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"223 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1109/tase.2026.3660357
Ya Liu, Yueer Wu, Fan Zhang, Panfeng Huang, Yingbo Lu, Haitao Chang
{"title":"Collision-free Trajectory Generation and Robust Nonlinear Distributed Model Predictive Control for Tethered Multi-rotor Unmanned Aerial Vehicles","authors":"Ya Liu, Yueer Wu, Fan Zhang, Panfeng Huang, Yingbo Lu, Haitao Chang","doi":"10.1109/tase.2026.3660357","DOIUrl":"https://doi.org/10.1109/tase.2026.3660357","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"34 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1109/tase.2026.3660614
Jun Zhang, Sheik M. Mohiuddin, Junjian Qi
{"title":"Distributed Optimal Control for Grid-Forming and Grid-Feeding Converters in DC Microgrid","authors":"Jun Zhang, Sheik M. Mohiuddin, Junjian Qi","doi":"10.1109/tase.2026.3660614","DOIUrl":"https://doi.org/10.1109/tase.2026.3660614","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"117 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-03DOI: 10.1109/tase.2026.3660685
Jinsen Zhang, Xiaobing Nie, Jinde Cao, Liang Hua
{"title":"Finite-Time Multistability of Impulsive Hopfield Neural Networks Under New Impulsive Sequence Designs","authors":"Jinsen Zhang, Xiaobing Nie, Jinde Cao, Liang Hua","doi":"10.1109/tase.2026.3660685","DOIUrl":"https://doi.org/10.1109/tase.2026.3660685","url":null,"abstract":"","PeriodicalId":51060,"journal":{"name":"IEEE Transactions on Automation Science and Engineering","volume":"5 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146109882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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