Pub Date : 2025-03-17DOI: 10.1109/JSYST.2024.3525313
{"title":"IEEE Systems Journal Information for Authors","authors":"","doi":"10.1109/JSYST.2024.3525313","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3525313","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"C4-C4"},"PeriodicalIF":4.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10929691","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/JSYST.2024.3525315
{"title":"IEEE Systems Council Information","authors":"","doi":"10.1109/JSYST.2024.3525315","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3525315","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"C3-C3"},"PeriodicalIF":4.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10929694","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/JSYST.2024.3525309
{"title":"IEEE Systems Journal Publication Information","authors":"","doi":"10.1109/JSYST.2024.3525309","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3525309","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"C2-C2"},"PeriodicalIF":4.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10929684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent research has shown it is possible for groups of robots to automatically “bootstrap” their own collective motion behaviors, particularly movement in a group. However, existing work has primarily provided proof of concept in regular, open arenas without obstacles. For practical applications on real robots, multiple collective motion skills are required. This article proposes a novel, multitask deep reinforcement learning algorithm and domain transfer architecture permitting multiple collective motion skills to be bootstrapped automatically and applied to real robots. The proposed approach is tested for tuning two collective motion skills for grouped movement and obstacle avoidance, without requiring a map of the environment. We show that our approach can tune obstacle avoidance parameters while maintaining high-quality swarming collective behavior when an obstacle is detected. Furthermore, learned collective motion skills can be transferred from a point mass simulation onto real mobile robots using our domain transfer architecture, without loss of quality. Transferability is comparable to that of an evolutionary algorithm run in a high-fidelity simulator.
{"title":"A Dual-Task Deep Reinforcement Learning and Domain Transfer Architecture for Bootstrapping Swarming Collective Motion Skills","authors":"Shadi Abpeikar;Matt Garratt;Sreenatha Anavatti;Reda Ghanem;Kathryn Kasmarik","doi":"10.1109/JSYST.2025.3536783","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3536783","url":null,"abstract":"Recent research has shown it is possible for groups of robots to automatically “bootstrap” their own collective motion behaviors, particularly movement in a group. However, existing work has primarily provided proof of concept in regular, open arenas without obstacles. For practical applications on real robots, multiple collective motion skills are required. This article proposes a novel, multitask deep reinforcement learning algorithm and domain transfer architecture permitting multiple collective motion skills to be bootstrapped automatically and applied to real robots. The proposed approach is tested for tuning two collective motion skills for grouped movement and obstacle avoidance, without requiring a map of the environment. We show that our approach can tune obstacle avoidance parameters while maintaining high-quality swarming collective behavior when an obstacle is detected. Furthermore, learned collective motion skills can be transferred from a point mass simulation onto real mobile robots using our domain transfer architecture, without loss of quality. Transferability is comparable to that of an evolutionary algorithm run in a high-fidelity simulator.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"327-338"},"PeriodicalIF":4.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-02-28DOI: 10.1109/JSYST.2025.3529705
Changyi Xu;Yuming Huo;Chunkun Shi;Ying Zhao
It is crucial to monitor the operational status of aeroengines by using the digital twin technology to realize virtual-real synchronization for the gas path system. The challenge is to accurately monitor deep parameters in real-time during synchronization, although existing digital twin technologies have made good progress in monitoring shallow parameters. This study proposes a virtual-real synchronization method for digital twins of an aeroengine gas path system (AGPS) based on deep reinforcement learning (RL). First, the parameters are divided into directly measurable parameters (DMP) and nondirectly measurable parameters (NDMP). Then, different algorithms are applied to different types of parameters. An unscented Kalman filtering algorithm is utilized to aid in the synchronization of the DMP. An RL approach is employed to train parameter inference models for the NDMP. By combining the two algorithms, synchronization between these two parameter classes is achieved. This method excels by integrating the NDMP into the virtual-real synchronization of the AGPS digital twin, concurrently reducing the inference time for this specific segment. Comparative experiments are conducted, and the results indicate an effective improvement in the accuracy of parameter inference with the proposed method. Simultaneously, it ensures real-time and robust parameter inference.
{"title":"Digital Twin Virtual-Real Synchronization for Aeroengine Gas Path System Based on Deep Reinforcement Learning","authors":"Changyi Xu;Yuming Huo;Chunkun Shi;Ying Zhao","doi":"10.1109/JSYST.2025.3529705","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3529705","url":null,"abstract":"It is crucial to monitor the operational status of aeroengines by using the digital twin technology to realize virtual-real synchronization for the gas path system. The challenge is to accurately monitor deep parameters in real-time during synchronization, although existing digital twin technologies have made good progress in monitoring shallow parameters. This study proposes a virtual-real synchronization method for digital twins of an aeroengine gas path system (AGPS) based on deep reinforcement learning (RL). First, the parameters are divided into directly measurable parameters (DMP) and nondirectly measurable parameters (NDMP). Then, different algorithms are applied to different types of parameters. An unscented Kalman filtering algorithm is utilized to aid in the synchronization of the DMP. An RL approach is employed to train parameter inference models for the NDMP. By combining the two algorithms, synchronization between these two parameter classes is achieved. This method excels by integrating the NDMP into the virtual-real synchronization of the AGPS digital twin, concurrently reducing the inference time for this specific segment. Comparative experiments are conducted, and the results indicate an effective improvement in the accuracy of parameter inference with the proposed method. Simultaneously, it ensures real-time and robust parameter inference.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"75-86"},"PeriodicalIF":4.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-02-26DOI: 10.1109/JSYST.2025.3532232
Bilel Ben Saoud;Leïla Nasraoui
Unmanned aerial vehicles (UAVs) play a pivotal role in 5G/6G wireless communication systems due to their deployment flexibility. This article explores optimal UAV positioning to maximize coverage in hybrid aerial–ground communication links. Exploiting a probabilistic line-of-sight (LOS) model, we examine coverage radius behavior in mixed urban and suburban environments to meet specific quality-of-service (QoS) targets. The analysis reveals that the coverage radius expands as the probability of LOS increases, which in turn increases with the UAV height. However, beyond a certain height, path loss becomes dominant, and further increases in altitude negatively impact the coverage radius. By studying the maximum coverage radius for minimum signal strength and spectral efficiency requirements, we numerically determine a configuration space of UAV altitudes and the corresponding maximum radius that satisfies the target QoS. The results illustrate a dual-regime behavior, where coverage increases with altitude up to a certain value, beyond which it declines, indicating the existence of an optimal altitude for reliability. In addition, the analysis of ground surface effects shows that flying over concrete surfaces significantly enhances coverage, offering a radius up to five times larger compared to rough, vegetated surfaces.
{"title":"Coverage Optimization for Reliable UAV-Assisted 5G/6G Communication Systems","authors":"Bilel Ben Saoud;Leïla Nasraoui","doi":"10.1109/JSYST.2025.3532232","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3532232","url":null,"abstract":"Unmanned aerial vehicles (UAVs) play a pivotal role in 5G/6G wireless communication systems due to their deployment flexibility. This article explores optimal UAV positioning to maximize coverage in hybrid aerial–ground communication links. Exploiting a probabilistic line-of-sight (LOS) model, we examine coverage radius behavior in mixed urban and suburban environments to meet specific quality-of-service (QoS) targets. The analysis reveals that the coverage radius expands as the probability of LOS increases, which in turn increases with the UAV height. However, beyond a certain height, path loss becomes dominant, and further increases in altitude negatively impact the coverage radius. By studying the maximum coverage radius for minimum signal strength and spectral efficiency requirements, we numerically determine a configuration space of UAV altitudes and the corresponding maximum radius that satisfies the target QoS. The results illustrate a dual-regime behavior, where coverage increases with altitude up to a certain value, beyond which it declines, indicating the existence of an optimal altitude for reliability. In addition, the analysis of ground surface effects shows that flying over concrete surfaces significantly enhances coverage, offering a radius up to five times larger compared to rough, vegetated surfaces.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"65-74"},"PeriodicalIF":4.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-02-24DOI: 10.1109/JSYST.2025.3532508
Antônio Sobrinho Campolina Martins;Leandro Ramos de Araujo;Débora Rosana Ribeiro Penido
This article proposes a new method to optimize the number of clusters (NoC) in the active distance-based clustering multiphase probabilistic power flow (MPPF). The objective is to determine a NoC that highly accurately promotes output variables without overloading the computational time. The method is based on intracluster and intercluster distance evaluations to achieve a good partition. A quasi-convex curve is formed to select the optimal NoC, ensuring an excellent computational time to converge. Tests are carried out using K-means, and simulations are conducted using IEEE unbalanced test feeders. Different input random variables are tested, including correlated and noncorrelated variables, with and without renewable distributed generators. The results prove that the input conditions significantly affect the optimal NoC. Comparisons are made with Monte Carlo simulation to justify the proposed application, showing that the computational time reduction provided by the clustering algorithm reaches up to ∼99% . Since the optimal NoC increases dramatically with the size of the input database, guidelines are proposed to reduce the MPPF dimensionality for more effective probabilistic procedures.
{"title":"Quasi-Convex NoC Optimization in the Active Multiphase Probabilistic Power Flow","authors":"Antônio Sobrinho Campolina Martins;Leandro Ramos de Araujo;Débora Rosana Ribeiro Penido","doi":"10.1109/JSYST.2025.3532508","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3532508","url":null,"abstract":"This article proposes a new method to optimize the number of clusters (NoC) in the active distance-based clustering multiphase probabilistic power flow (MPPF). The objective is to determine a NoC that highly accurately promotes output variables without overloading the computational time. The method is based on intracluster and intercluster distance evaluations to achieve a good partition. A quasi-convex curve is formed to select the optimal NoC, ensuring an excellent computational time to converge. Tests are carried out using K-means, and simulations are conducted using IEEE unbalanced test feeders. Different input random variables are tested, including correlated and noncorrelated variables, with and without renewable distributed generators. The results prove that the input conditions significantly affect the optimal NoC. Comparisons are made with Monte Carlo simulation to justify the proposed application, showing that the computational time reduction provided by the clustering algorithm reaches up to ∼99% . Since the optimal NoC increases dramatically with the size of the input database, guidelines are proposed to reduce the MPPF dimensionality for more effective probabilistic procedures.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"294-304"},"PeriodicalIF":4.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-02-21DOI: 10.1109/JSYST.2025.3531837
Jianli Qiu;Zhufang Kuang;Zhenqi Huang;Siyu Lin
Mobile edge computing, a prospective wireless communication framework, can contribute to offload a large number of tasks to unmanned aerial vehicle (UAV) mobile edge servers. Besides, the demand for server computational resources increasingly ascends as the volume of processing tasks grows. However, in reality, many devices have similar computing tasks and require the same computing data. Therefore, servers can effectively reduce server computing latency and bandwidth costs by caching task data. This investigation explores task security offloading and data caching optimization strategies in scenarios with multiple interfering devices. With the goal of minimizing the total energy consumption, the UAV trajectories, transmission power, task offloading scheduling strategies, and caching decisions is jointly optimized. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To make this problem solved, the original problem is decomposed into three tiers, and an iterative algorithm named CDSFS which is based on the coordinate descent, successive convex approximation, and flow shop scheduling is proposed. Simulation results demonstrate the stability and superiority of the proposed algorithm.
{"title":"Security Offloading Scheduling and Caching Optimization Algorithm in UAV Edge Computing","authors":"Jianli Qiu;Zhufang Kuang;Zhenqi Huang;Siyu Lin","doi":"10.1109/JSYST.2025.3531837","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3531837","url":null,"abstract":"Mobile edge computing, a prospective wireless communication framework, can contribute to offload a large number of tasks to unmanned aerial vehicle (UAV) mobile edge servers. Besides, the demand for server computational resources increasingly ascends as the volume of processing tasks grows. However, in reality, many devices have similar computing tasks and require the same computing data. Therefore, servers can effectively reduce server computing latency and bandwidth costs by caching task data. This investigation explores task security offloading and data caching optimization strategies in scenarios with multiple interfering devices. With the goal of minimizing the total energy consumption, the UAV trajectories, transmission power, task offloading scheduling strategies, and caching decisions is jointly optimized. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To make this problem solved, the original problem is decomposed into three tiers, and an iterative algorithm named CDSFS which is based on the coordinate descent, successive convex approximation, and flow shop scheduling is proposed. Simulation results demonstrate the stability and superiority of the proposed algorithm.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"96-106"},"PeriodicalIF":4.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-02-18DOI: 10.1109/JSYST.2025.3532698
Jhonathan Prieto Rojas;Rayan Almazyad;Abdulaziz Al Hayyah;Ahmed Alruhaiman;Mohammed Almusharraf;Suhail Al-Dharrab;Hussein Attia
The underground layers of the Earth contain immense resources that require geophysical surveys. This article presents an end-to-end, self-powered wireless sensor network (WSN) for geophysical surveys. The WSN conducts geophysical surveys in an energy-efficient, portable manner. It includes a sensing element, advanced electronics, data processing and digitization, and wireless transmission with networking capabilities between sensing nodes. The system is equipped with a power management module with solar-powered charging capabilities, allowing for at least six days of effective operation on a few hours' worth of charge. The electronic circuitry performing amplification and filtering provides cut-off frequencies of 8.2–108 Hz, and the sensor node exhibits a sampling frequency of 600 SPS. Furthermore, the system implements power modes (active/sleep) to reduce power consumption, with a nominal power usage of only 650 mW at its maximum. The WSN comprises a multihop implementation with smart routing to ensure power-efficient and reliable data transmission. In addition, message encryption is implemented for enhanced wireless security. A field test was conducted to validate the proposed geophysical data acquisition system. Geophysical signals were detected and wirelessly transmitted over a 200 m2 area employing a network of six nodes to a storage unit, where they were successfully reconstructed and remained stored for later processing and analysis.
{"title":"Self-Powered End-to-End Wireless Sensor Network for Geophysical Explorations","authors":"Jhonathan Prieto Rojas;Rayan Almazyad;Abdulaziz Al Hayyah;Ahmed Alruhaiman;Mohammed Almusharraf;Suhail Al-Dharrab;Hussein Attia","doi":"10.1109/JSYST.2025.3532698","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3532698","url":null,"abstract":"The underground layers of the Earth contain immense resources that require geophysical surveys. This article presents an end-to-end, self-powered wireless sensor network (WSN) for geophysical surveys. The WSN conducts geophysical surveys in an energy-efficient, portable manner. It includes a sensing element, advanced electronics, data processing and digitization, and wireless transmission with networking capabilities between sensing nodes. The system is equipped with a power management module with solar-powered charging capabilities, allowing for at least six days of effective operation on a few hours' worth of charge. The electronic circuitry performing amplification and filtering provides cut-off frequencies of 8.2–108 Hz, and the sensor node exhibits a sampling frequency of 600 SPS. Furthermore, the system implements power modes (active/sleep) to reduce power consumption, with a nominal power usage of only 650 mW at its maximum. The WSN comprises a multihop implementation with smart routing to ensure power-efficient and reliable data transmission. In addition, message encryption is implemented for enhanced wireless security. A field test was conducted to validate the proposed geophysical data acquisition system. Geophysical signals were detected and wirelessly transmitted over a 200 m<sup>2</sup> area employing a network of six nodes to a storage unit, where they were successfully reconstructed and remained stored for later processing and analysis.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"107-118"},"PeriodicalIF":4.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2025-02-13DOI: 10.1109/JSYST.2025.3533880
Zihan Chen;Shengda Tang
This study delves into the distributed linear quadratic tracking (LQT) problem within interconnected multimachine power systems (IMMPSs), and proposes a model-free policy iteration (PI) algorithm based on data compression technology for designing sparse controllers that align with the actual communication links in IMMPSs. Specifically, to address the practical limitation that communication links between subsystems of IMMPSs may be unavailable, we first formulate a sparse LQT problem in which the sparse patterns of controllers match the actual communication links. Meanwhile, in order to be applicable to real-time applications while overcoming model uncertainty caused by parameter variability common in IMMPSs models, we subsequently develop a data compression-based model-free PI algorithm for the abovementioned sparse LQT problem. The main advantages of this algorithm over existing algorithms for IMMPSs control are threefold: first, it has the ability to operate without a prior knowledge of system model, second, its embedded data compression significantly reduces the time consumption for controller design, making it suitable for real-time applications, and third, it designs controllers based on actual communication links, making it practical for applications where communication infrastructure may be constrained. Finally the efficacy of the proposed algorithm is verified through the IEEE 39-bus New England Power System.
{"title":"Data Compression-Based Model-Free PI Algorithm for Sparse LQT Control in Interconnected Multimachine Power Systems","authors":"Zihan Chen;Shengda Tang","doi":"10.1109/JSYST.2025.3533880","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3533880","url":null,"abstract":"This study delves into the distributed linear quadratic tracking (LQT) problem within interconnected multimachine power systems (IMMPSs), and proposes a model-free policy iteration (PI) algorithm based on data compression technology for designing sparse controllers that align with the actual communication links in IMMPSs. Specifically, to address the practical limitation that communication links between subsystems of IMMPSs may be unavailable, we first formulate a sparse LQT problem in which the sparse patterns of controllers match the actual communication links. Meanwhile, in order to be applicable to real-time applications while overcoming model uncertainty caused by parameter variability common in IMMPSs models, we subsequently develop a data compression-based model-free PI algorithm for the abovementioned sparse LQT problem. The main advantages of this algorithm over existing algorithms for IMMPSs control are threefold: first, it has the ability to operate without a prior knowledge of system model, second, its embedded data compression significantly reduces the time consumption for controller design, making it suitable for real-time applications, and third, it designs controllers based on actual communication links, making it practical for applications where communication infrastructure may be constrained. Finally the efficacy of the proposed algorithm is verified through the IEEE 39-bus New England Power System.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"176-187"},"PeriodicalIF":4.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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