Pub Date : 2024-07-31DOI: 10.1109/jsyst.2024.3432449
Dadmehr Rahbari, Foisal Ahmed, Maksim Jenihhin, Muhammad Mahtab Alam, Yannick Le Moullec
{"title":"Reliability-Critical Computation Offloading in UAV Swarms","authors":"Dadmehr Rahbari, Foisal Ahmed, Maksim Jenihhin, Muhammad Mahtab Alam, Yannick Le Moullec","doi":"10.1109/jsyst.2024.3432449","DOIUrl":"https://doi.org/10.1109/jsyst.2024.3432449","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"49 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865014","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 : 2024-07-30DOI: 10.1109/JSYST.2024.3422284
Yi Chen;Tianyi Wu;Xiaobing Ma;Jingjing Wang;Rui Peng;Li Yang
Structural dependency, as widely existed in complex engineering equipment, refers to the structural intervention between components so that replacing a component requires the removal of others on its disassembly path. Naturally, it is cost-efficient to cluster maintenance jobs to share disassembly time and reduce system downtime. However, maintenance management by particularly considering the disassembly structure is rarely reported in the literature. To address such deficiency, we propose an innovative dependency-specific maintenance policy, which realizes the global union of “static” scheduled block maintenance (SBM) and “dynamic” opportunistic maintenance (OM). SBM coordinates preventive maintenance jobs in conjunction, which forms the basic policy framework. OM decides which components are opportunistically replaced in case of failure, which fine-tunes the framework to further exploit the dependency. Motivated by the fractal nature of disassembly structure, we develop a dynamic-programming-based optimization approach, which enables: 1) the joint optimization of model parameters in a sequential manner, and 2) an efficient optimization applicable to large-scale equipment. We demonstrate the model through a case study in the maintenance management of high-speed train bogies. The results show that the proposed policy significantly promotes system availability by coordinating replacement intervals within the same disassembly subtree, and effectively reducing downtime by integrating SBM with OM.
{"title":"System Maintenance Optimization Under Structural Dependency: A Dynamic Grouping Approach","authors":"Yi Chen;Tianyi Wu;Xiaobing Ma;Jingjing Wang;Rui Peng;Li Yang","doi":"10.1109/JSYST.2024.3422284","DOIUrl":"10.1109/JSYST.2024.3422284","url":null,"abstract":"Structural dependency, as widely existed in complex engineering equipment, refers to the structural intervention between components so that replacing a component requires the removal of others on its disassembly path. Naturally, it is cost-efficient to cluster maintenance jobs to share disassembly time and reduce system downtime. However, maintenance management by particularly considering the disassembly structure is rarely reported in the literature. To address such deficiency, we propose an innovative dependency-specific maintenance policy, which realizes the global union of “static” scheduled block maintenance (SBM) and “dynamic” opportunistic maintenance (OM). SBM coordinates preventive maintenance jobs in conjunction, which forms the basic policy framework. OM decides which components are opportunistically replaced in case of failure, which fine-tunes the framework to further exploit the dependency. Motivated by the fractal nature of disassembly structure, we develop a dynamic-programming-based optimization approach, which enables: 1) the joint optimization of model parameters in a sequential manner, and 2) an efficient optimization applicable to large-scale equipment. We demonstrate the model through a case study in the maintenance management of high-speed train bogies. The results show that the proposed policy significantly promotes system availability by coordinating replacement intervals within the same disassembly subtree, and effectively reducing downtime by integrating SBM with OM.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1605-1616"},"PeriodicalIF":4.0,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141865013","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 : 2024-07-25DOI: 10.1109/JSYST.2024.3409231
Ali K. Raz;Mohammed Bhuyian;Jose L. Bricio-Neto;Christopher Santos;Daniel Maxwell
Mission engineering (ME) is an emerging approach to designing and analyzing configurations of system-of-systems (SoS) for accomplishing one or more missions. ME seeks to flexibly leverage SoS capabilities and dynamically adapt their configuration to meet evolving mission needs. SoS configurations today, however, remain static and are carefully designed to accomplish a mission. The problem we address in this article is developing modeling and analysis techniques for flexible integration and adaptive selection of potential SoS configurations to achieve multiple missions with an overarching agility in the execution space. We propose a foundational framework for ME, complete with semantics and grammar to represent the ME design space (MEDS), along with a set of logical and mathematical modeling approaches that lends the MEDS to robust SoS analytical methods. Specifically, the framework proposes development of a mission-focused ontology and domain-specific language to enable consistent semantic representation of MEDS, which is then logically evaluated for spatial and temporal consistency in forming SoS configurations using set-based design principles and Allen's interval algebra. The resulting feasible SoS configurations are then evaluated for mission success using graph theory and multiattribute utility theory. The application of the framework is demonstrated on a simplified and notional sense-decide-effect problem for flexibly accomplishing multiple missions with SoS.
任务工程（ME）是一种新兴的方法，用于设计和分析用于完成一项或多项任务的系统配置（SoS）。任务工程旨在灵活利用 SoS 的能力，动态调整其配置，以满足不断变化的任务需求。然而，如今的 SoS 配置仍然是静态的，是为完成任务而精心设计的。我们在本文中要解决的问题是开发建模和分析技术，以便灵活集成和自适应选择潜在的 SoS 配置，从而在执行空间中以总体敏捷性完成多种任务。我们为 ME 提出了一个基础框架，其中包含表示 ME 设计空间（MEDS）的语义和语法，以及一套逻辑和数学建模方法，可将 MEDS 借用于稳健的 SoS 分析方法。具体来说，该框架建议开发一种以任务为重点的本体论和特定领域语言，以实现 MEDS 的一致语义表述，然后使用基于集合的设计原则和艾伦区间代数对其进行逻辑评估，以确定在形成 SoS 配置时的空间和时间一致性。然后，利用图论和多属性效用理论对由此产生的可行 SoS 配置进行任务成功率评估。该框架的应用在一个简化和概念化的感知-决定-效应问题上进行了演示，该问题旨在利用 SoS 灵活完成多个任务。
{"title":"Conceptual, Mathematical, and Analytical Foundations for Mission Engineering and System of Systems Analysis","authors":"Ali K. Raz;Mohammed Bhuyian;Jose L. Bricio-Neto;Christopher Santos;Daniel Maxwell","doi":"10.1109/JSYST.2024.3409231","DOIUrl":"10.1109/JSYST.2024.3409231","url":null,"abstract":"Mission engineering (ME) is an emerging approach to designing and analyzing configurations of system-of-systems (SoS) for accomplishing one or more missions. ME seeks to flexibly leverage SoS capabilities and dynamically adapt their configuration to meet evolving mission needs. SoS configurations today, however, remain static and are carefully designed to accomplish a mission. The problem we address in this article is developing modeling and analysis techniques for flexible integration and adaptive selection of potential SoS configurations to achieve multiple missions with an overarching agility in the execution space. We propose a foundational framework for ME, complete with semantics and grammar to represent the ME design space (MEDS), along with a set of logical and mathematical modeling approaches that lends the MEDS to robust SoS analytical methods. Specifically, the framework proposes development of a mission-focused ontology and domain-specific language to enable consistent semantic representation of MEDS, which is then logically evaluated for spatial and temporal consistency in forming SoS configurations using set-based design principles and Allen's interval algebra. The resulting feasible SoS configurations are then evaluated for mission success using graph theory and multiattribute utility theory. The application of the framework is demonstrated on a simplified and notional sense-decide-effect problem for flexibly accomplishing multiple missions with SoS.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1549-1559"},"PeriodicalIF":4.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10609409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782840","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 : 2024-07-23DOI: 10.1109/jsyst.2024.3426096
Yijiu Li, Dang Van Huynh, Van-Linh Nguyen, Dac-Binh Ha, Hans-Jürgen Zepernick, Trung Q. Duong
{"title":"Multiagent UAV-Aided URLLC Mobile Edge Computing Systems: A Joint Communication and Computation Optimization Approach","authors":"Yijiu Li, Dang Van Huynh, Van-Linh Nguyen, Dac-Binh Ha, Hans-Jürgen Zepernick, Trung Q. Duong","doi":"10.1109/jsyst.2024.3426096","DOIUrl":"https://doi.org/10.1109/jsyst.2024.3426096","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"71 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782841","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 : 2024-07-19DOI: 10.1109/JSYST.2024.3420237
Mohammad Jadidbonab;Hussein Abdeltawab;Yasser Abdel-Rady I. Mohamed
This article develops an operational framework for hydrogen microgrids integrated with traffic and power networks to optimize decision-making strategies. It tackles challenges in traffic flow prediction exacerbated by the rise of electric and hydrogen vehicles, which significantly affect power systems and hydrogen microgrids. We employ a risk-averse information gap decision theory to ensure secure operations under uncertain traffic conditions. Our framework utilizes a hybrid deep-learning forecasting method, combining a 1-D convolutional neural network and bidirectional long short-term memory to accurately predict traffic flow for origin–destination pairs in Edmonton, Canada. Enhanced by a Bayesian algorithm for hyperparameter tuning, this method improves prediction accuracy and operational efficiency. The framework also integrates operational strategies with urban travel plans to optimize charging for electric and hydrogen vehicles, thereby enhancing energy efficiency and supporting thermal demands. Validated in Edmonton's power and traffic networks, our framework enhances optimal charging, routing, and operation conditions, surpassing traditional methods to maintain secure operations during outages and improve the overall system robustness.
{"title":"A Hybrid Traffic Flow Forecasting and Risk-Averse Decision Strategy for Hydrogen-Based Integrated Traffic and Power Networks","authors":"Mohammad Jadidbonab;Hussein Abdeltawab;Yasser Abdel-Rady I. Mohamed","doi":"10.1109/JSYST.2024.3420237","DOIUrl":"10.1109/JSYST.2024.3420237","url":null,"abstract":"This article develops an operational framework for hydrogen microgrids integrated with traffic and power networks to optimize decision-making strategies. It tackles challenges in traffic flow prediction exacerbated by the rise of electric and hydrogen vehicles, which significantly affect power systems and hydrogen microgrids. We employ a risk-averse information gap decision theory to ensure secure operations under uncertain traffic conditions. Our framework utilizes a hybrid deep-learning forecasting method, combining a 1-D convolutional neural network and bidirectional long short-term memory to accurately predict traffic flow for origin–destination pairs in Edmonton, Canada. Enhanced by a Bayesian algorithm for hyperparameter tuning, this method improves prediction accuracy and operational efficiency. The framework also integrates operational strategies with urban travel plans to optimize charging for electric and hydrogen vehicles, thereby enhancing energy efficiency and supporting thermal demands. Validated in Edmonton's power and traffic networks, our framework enhances optimal charging, routing, and operation conditions, surpassing traditional methods to maintain secure operations during outages and improve the overall system robustness.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1581-1592"},"PeriodicalIF":4.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737277","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 : 2024-07-18DOI: 10.1109/jsyst.2024.3424259
Abdullah Lakhan, Mazin Abed Mohammed, Dilovan Asaad Zebar, Karrar Hameed Abdulkareem, Muhammet Deveci, Haydar Abdulameer Marhoon, Jan Nedoma, Radek Martinek
{"title":"DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows","authors":"Abdullah Lakhan, Mazin Abed Mohammed, Dilovan Asaad Zebar, Karrar Hameed Abdulkareem, Muhammet Deveci, Haydar Abdulameer Marhoon, Jan Nedoma, Radek Martinek","doi":"10.1109/jsyst.2024.3424259","DOIUrl":"https://doi.org/10.1109/jsyst.2024.3424259","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"9 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746436","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 : 2024-07-17DOI: 10.1109/JSYST.2024.3425541
Baya Cherif;Hakim Ghazzai;Ahmad Alsharoa
Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct road user detection. Then, we implement advanced LiDAR fusion techniques, including raw data fusion and decision data fusion, in an endeavor to improve detection performance through the combined analysis of multiple A-LiD systems. Finally, we employ the unscented Kalman Filter for object tracking and position estimation. We present selected simulation outcomes to demonstrate the effectiveness of our proposed solution. A comparison between the two fusion methods shows that raw point cloud fusion provides better detection performance than decision fusion.
{"title":"LiDAR From the Sky: UAV Integration and Fusion Techniques for Advanced Traffic Monitoring","authors":"Baya Cherif;Hakim Ghazzai;Ahmad Alsharoa","doi":"10.1109/JSYST.2024.3425541","DOIUrl":"10.1109/JSYST.2024.3425541","url":null,"abstract":"Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct road user detection. Then, we implement advanced LiDAR fusion techniques, including raw data fusion and decision data fusion, in an endeavor to improve detection performance through the combined analysis of multiple A-LiD systems. Finally, we employ the unscented Kalman Filter for object tracking and position estimation. We present selected simulation outcomes to demonstrate the effectiveness of our proposed solution. A comparison between the two fusion methods shows that raw point cloud fusion provides better detection performance than decision fusion.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1639-1650"},"PeriodicalIF":4.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737276","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}
{"title":"Multiagent Detection System Based on Spatial Adaptive Feature Aggregation","authors":"Hongbo Wang, He Wang, Xin Zhang, Runze Ruan, Yueyun Wang, Yuyu Yin","doi":"10.1109/jsyst.2024.3423752","DOIUrl":"https://doi.org/10.1109/jsyst.2024.3423752","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"64 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718853","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 : 2024-07-08DOI: 10.1109/JSYST.2024.3420950
Ali Salehpour;Irfan Al-Anbagi
Cascading failures resulting from cyberattacks are one of the main concerns in smart grid systems. The use of machine learning (ML) algorithms has become more relevant in identifying and forecasting such cascading failures. In this article, we develop a real-time early stage mechanism (RESP) to predict cascading failures due to cyberattacks in smart grid systems using supervised ML algorithms. We use a realistic methodology to create a dataset to train the algorithms and predict the state of all components of the system after failure propagation. We utilize the extreme gradient boosting (XGBoost) algorithm and consider the features of both the power and communication networks to improve the failure prediction accuracy. We use the real-time digital simulator (RTDS) to simulate the power system and make the system more applicable. We evaluate the mechanism's effectiveness using the IEEE 14-bus system, which results in the XGBoost algorithm achieving a 96.25% prediction accuracy rate in random attacks. We show that RESP can accurately predict the state of a power system in the early stages of failure propagation using real-time data. Furthermore, we show that RESP can identify the initial failure locations, which can aid in further protection plans and decisions.
{"title":"RESP: A Real-Time Early Stage Prediction Mechanism for Cascading Failures in Smart Grid Systems","authors":"Ali Salehpour;Irfan Al-Anbagi","doi":"10.1109/JSYST.2024.3420950","DOIUrl":"10.1109/JSYST.2024.3420950","url":null,"abstract":"Cascading failures resulting from cyberattacks are one of the main concerns in smart grid systems. The use of machine learning (ML) algorithms has become more relevant in identifying and forecasting such cascading failures. In this article, we develop a real-time early stage mechanism (RESP) to predict cascading failures due to cyberattacks in smart grid systems using supervised ML algorithms. We use a realistic methodology to create a dataset to train the algorithms and predict the state of all components of the system after failure propagation. We utilize the extreme gradient boosting (XGBoost) algorithm and consider the features of both the power and communication networks to improve the failure prediction accuracy. We use the real-time digital simulator (RTDS) to simulate the power system and make the system more applicable. We evaluate the mechanism's effectiveness using the IEEE 14-bus system, which results in the XGBoost algorithm achieving a 96.25% prediction accuracy rate in random attacks. We show that RESP can accurately predict the state of a power system in the early stages of failure propagation using real-time data. Furthermore, we show that RESP can identify the initial failure locations, which can aid in further protection plans and decisions.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1593-1604"},"PeriodicalIF":4.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570300","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}
This study delves into the distributed optimal coordination (DOC) problem, where a network comprises agents with different relative degrees. Each agent is equipped with a private cost function. The goal is to steer these agents towards minimizing the global cost function, which aggregates their individual costs. Existing literature often leans on known agent dynamics, which may not faithfully represent real-world scenarios. To bridge this gap, we delve into the DOC problem within a network of linear time-invariant (LTI) agents, where the system matrices remain entirely unknown. Our proposed solution introduces a novel distributed two-layer control policy: the top layer endeavors to find the minimizer and generates tailored reference signals for each agent, while the bottom layer equips each agent with an adaptive controller to track these references. Key assumptions include strongly convex private cost functions with local Lipschitz gradients. Under these conditions, our control policy guarantees asymptotic consensus on the global minimizer within the network. Moreover, the control policy operates fully distributedly, relying solely on private and neighbor information for execution. Theoretical insights are substantiated through simulations, encompassing both numerical and practical examples involving speed control of a multimotor network, thereby affirming the efficacy of our approach in practical settings.
{"title":"Heterogeneous Unknown Multiagent Systems of Different Relative Degrees: A Distributed Optimal Coordination Design","authors":"Hossein Noorighanavati Zadeh;Reza Naseri;Mohammad Bagher Menhaj;Amir Abolfazl Suratgar","doi":"10.1109/JSYST.2024.3417255","DOIUrl":"10.1109/JSYST.2024.3417255","url":null,"abstract":"This study delves into the distributed optimal coordination (DOC) problem, where a network comprises agents with different relative degrees. Each agent is equipped with a private cost function. The goal is to steer these agents towards minimizing the global cost function, which aggregates their individual costs. Existing literature often leans on known agent dynamics, which may not faithfully represent real-world scenarios. To bridge this gap, we delve into the DOC problem within a network of linear time-invariant (LTI) agents, where the system matrices remain entirely unknown. Our proposed solution introduces a novel distributed two-layer control policy: the top layer endeavors to find the minimizer and generates tailored reference signals for each agent, while the bottom layer equips each agent with an adaptive controller to track these references. Key assumptions include strongly convex private cost functions with local Lipschitz gradients. Under these conditions, our control policy guarantees asymptotic consensus on the global minimizer within the network. Moreover, the control policy operates fully distributedly, relying solely on private and neighbor information for execution. Theoretical insights are substantiated through simulations, encompassing both numerical and practical examples involving speed control of a multimotor network, thereby affirming the efficacy of our approach in practical settings.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1570-1580"},"PeriodicalIF":4.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503104","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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