Pub Date : 2025-03-20DOI: 10.1109/TCNS.2025.3548567
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/TCNS.2025.3548567","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3548567","url":null,"abstract":"","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 1","pages":"C2-C2"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935611","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676144","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 : 2025-03-20DOI: 10.1109/TCNS.2025.3548568
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/TCNS.2025.3548568","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3548568","url":null,"abstract":"","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 1","pages":"1199-1200"},"PeriodicalIF":4.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667717","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 : 2025-02-07DOI: 10.1109/TCNS.2025.3539542
{"title":"2024 Index IEEE Transactions on Control of Network Systems Vol. 11","authors":"","doi":"10.1109/TCNS.2025.3539542","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3539542","url":null,"abstract":"","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"11 4","pages":"1-38"},"PeriodicalIF":4.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10878379","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361275","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 : 2025-01-31DOI: 10.1109/TCNS.2025.3526701
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/TCNS.2025.3526701","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526701","url":null,"abstract":"","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"11 4","pages":"C2-C2"},"PeriodicalIF":4.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10865804","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361453","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 : 2025-01-31DOI: 10.1109/TCNS.2025.3526702
{"title":"IEEE Control Systems Society Information","authors":"","doi":"10.1109/TCNS.2025.3526702","DOIUrl":"https://doi.org/10.1109/TCNS.2025.3526702","url":null,"abstract":"","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"11 4","pages":"2276-2277"},"PeriodicalIF":4.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10865829","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143107205","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}
In this article, we deal with a network of agents that want to cooperatively minimize the sum of local cost functions depending on a common decision variable. We consider the challenging scenario in which objective functions are unknown and agents have only access to local measurements of their local functions. We propose a novel distributed algorithm that combines a recent gradient tracking policy with an extremum seeking technique to estimate the global descent direction. The joint use of these two techniques results in a distributed optimization scheme that provides arbitrarily accurate solution estimates through the combination of Lyapunov and averaging analysis approaches with consensus theory. We perform numerical simulations in a personalized optimization framework to corroborate the theoretical results.
{"title":"Extremum Seeking Tracking for Derivative-Free Distributed Optimization","authors":"Nicola Mimmo;Guido Carnevale;Andrea Testa;Giuseppe Notarstefano","doi":"10.1109/TCNS.2024.3510368","DOIUrl":"https://doi.org/10.1109/TCNS.2024.3510368","url":null,"abstract":"In this article, we deal with a network of agents that want to cooperatively minimize the sum of local cost functions depending on a common decision variable. We consider the challenging scenario in which objective functions are unknown and agents have only access to local measurements of their local functions. We propose a novel distributed algorithm that combines a recent gradient tracking policy with an extremum seeking technique to estimate the global descent direction. The joint use of these two techniques results in a distributed optimization scheme that provides arbitrarily accurate solution estimates through the combination of Lyapunov and averaging analysis approaches with consensus theory. We perform numerical simulations in a personalized optimization framework to corroborate the theoretical results.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 1","pages":"584-595"},"PeriodicalIF":4.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10772652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688041","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 : 2024-12-03DOI: 10.1109/TCNS.2024.3510602
Christian David Rodríguez-Camargo;Andrés F. Urquijo-Rodríguez;Eduardo Mojica-Nava
Multilayer networks provide a more comprehensive framework for exploring real-world and engineering systems than traditional single-layer networks consisting of multiple interacting networks. However, despite significant research on distributed optimization for single-layer networks, similar progress is lacking for multilayer systems. This article proposes two algorithms for distributed optimization problems in multiplex networks using the supra-Laplacian matrix and its diffusion dynamics. The algorithms include a distributed saddle-point algorithm and its variation as a distributed gradient descent algorithm. By relating consensus and diffusion dynamics, we obtain the multiplex supra-Laplacian matrix. We extend the distributed gradient descent algorithm for multiplex networks using this matrix and analyze the convergence of both algorithms with several theoretical results. Numerical examples validate our proposed algorithms, and we explore the impact of interlayer diffusion on consensus time. We also present a coordinated dispatch for interdependent infrastructure networks (energy–gas) to demonstrate the application of the proposed framework to real engineering problems.
{"title":"Consensus-Based Distributed Optimization for Multiagent Systems Over Multiplex Networks","authors":"Christian David Rodríguez-Camargo;Andrés F. Urquijo-Rodríguez;Eduardo Mojica-Nava","doi":"10.1109/TCNS.2024.3510602","DOIUrl":"https://doi.org/10.1109/TCNS.2024.3510602","url":null,"abstract":"Multilayer networks provide a more comprehensive framework for exploring real-world and engineering systems than traditional single-layer networks consisting of multiple interacting networks. However, despite significant research on distributed optimization for single-layer networks, similar progress is lacking for multilayer systems. This article proposes two algorithms for distributed optimization problems in multiplex networks using the supra-Laplacian matrix and its diffusion dynamics. The algorithms include a distributed saddle-point algorithm and its variation as a distributed gradient descent algorithm. By relating consensus and diffusion dynamics, we obtain the multiplex supra-Laplacian matrix. We extend the distributed gradient descent algorithm for multiplex networks using this matrix and analyze the convergence of both algorithms with several theoretical results. Numerical examples validate our proposed algorithms, and we explore the impact of interlayer diffusion on consensus time. We also present a coordinated dispatch for interdependent infrastructure networks (energy–gas) to demonstrate the application of the proposed framework to real engineering problems.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 1","pages":"1040-1051"},"PeriodicalIF":4.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667364","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 : 2024-12-03DOI: 10.1109/TCNS.2024.3510353
Zijie Guo;Hongru Ren;Hongyi Li;Tingwen Huang
An optimal event-driven containment control problem is studied for partially unknown nonlinear multiagent systems with input constraints and state constraints. Its novelty lies in the optimization of the performance index while ensuring constraints handling abilities on states and inputs. First, an improved discounted cost function is constructed, and the state and input constraint information are encoded into the cost function by barrier functions and nonquadratic utility functions, respectively. Then, the approximate distributed optimal containment control policy is derived by an integral reinforcement learning (IRL)-based adaptive critic design, where the IRL technique can overcome the limitation of known drift dynamics in previous results. In critic neural networks learning, the weight tuning law is presented by virtue of the concurrent learning technique, which relaxes the persistence of excitation conditions by storing appropriate historical data. In order to reduce the amount of information transmitted through the controller-to-actuator channel, a containment error-dependent dynamic event-triggered mechanism is defined. Theoretical results indicate that signals in closed-loop systems driven by event-triggered optimal controllers are uniformly ultimately bounded, and Zeno behavior is avoided. Finally, the effectiveness of the developed method is illustrated by a simulation example on multiple single-link robot manipulators.
{"title":"Event-Based Optimal Containment Control for Constrained Multiagent Systems Using Integral Reinforcement Learning","authors":"Zijie Guo;Hongru Ren;Hongyi Li;Tingwen Huang","doi":"10.1109/TCNS.2024.3510353","DOIUrl":"https://doi.org/10.1109/TCNS.2024.3510353","url":null,"abstract":"An optimal event-driven containment control problem is studied for partially unknown nonlinear multiagent systems with input constraints and state constraints. Its novelty lies in the optimization of the performance index while ensuring constraints handling abilities on states and inputs. First, an improved discounted cost function is constructed, and the state and input constraint information are encoded into the cost function by barrier functions and nonquadratic utility functions, respectively. Then, the approximate distributed optimal containment control policy is derived by an integral reinforcement learning (IRL)-based adaptive critic design, where the IRL technique can overcome the limitation of known drift dynamics in previous results. In critic neural networks learning, the weight tuning law is presented by virtue of the concurrent learning technique, which relaxes the persistence of excitation conditions by storing appropriate historical data. In order to reduce the amount of information transmitted through the controller-to-actuator channel, a containment error-dependent dynamic event-triggered mechanism is defined. Theoretical results indicate that signals in closed-loop systems driven by event-triggered optimal controllers are uniformly ultimately bounded, and Zeno behavior is avoided. Finally, the effectiveness of the developed method is illustrated by a simulation example on multiple single-link robot manipulators.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 1","pages":"609-619"},"PeriodicalIF":4.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688098","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 : 2024-12-02DOI: 10.1109/TCNS.2024.3510578
Jie Zhong;Qinyao Pan;Die Xu;Bowen Li;Ning Li
In this article, the output regulation problem (ORP) of Boolean control networks (BCNs) is studied via the semitensor product of matrices, with consideration of probabilistic outputs from the reference Boolean networks (BNs). We first define the concept of mean square error (MSE) between outputs of two BNs, establishing several theoretical results on the solvability of the ORP. Subsequently, state feedback controllers are designed to address the ORP of BCNs under different scenarios. In the first scenario, ORP is addressed by developing control strategies that ensure the system's output remains within a predefined MSE threshold. In the second scenario, we focus on achieving the minimum MSE through the optimization of control strategies. The final scenario extends the second by conducting a detailed analysis to maintain this minimum MSE uniformly across all state pairs. To conclude, several numerical examples are provided to verify the effectiveness of the proposed theoretical results.
{"title":"Output Regulation of Boolean Control Networks Under Probabilistic Outputs","authors":"Jie Zhong;Qinyao Pan;Die Xu;Bowen Li;Ning Li","doi":"10.1109/TCNS.2024.3510578","DOIUrl":"https://doi.org/10.1109/TCNS.2024.3510578","url":null,"abstract":"In this article, the output regulation problem (ORP) of Boolean control networks (BCNs) is studied via the semitensor product of matrices, with consideration of probabilistic outputs from the reference Boolean networks (BNs). We first define the concept of mean square error (MSE) between outputs of two BNs, establishing several theoretical results on the solvability of the ORP. Subsequently, state feedback controllers are designed to address the ORP of BCNs under different scenarios. In the first scenario, ORP is addressed by developing control strategies that ensure the system's output remains within a predefined MSE threshold. In the second scenario, we focus on achieving the minimum MSE through the optimization of control strategies. The final scenario extends the second by conducting a detailed analysis to maintain this minimum MSE uniformly across all state pairs. To conclude, several numerical examples are provided to verify the effectiveness of the proposed theoretical results.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 1","pages":"28-37"},"PeriodicalIF":4.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676102","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 : 2024-12-02DOI: 10.1109/TCNS.2024.3510364
Yu Chen;Zhi-Wei Liu;Guanghui Wen;Yan-Wu Wang
Fast line outages detection is crucial for the safe operation of power systems. This article proposes a novel algorithm for detecting multiple line outages (MLOs) using a distributed finite-time observer, which overcomes the limitations of existing methods that rely on difficult-to-obtain information, such as rotor inertia and damping ratio. The proposed algorithm relies solely on local measurements and information from neighbors to update each node's local observer. Rigorous mathematical analysis validates that the finite-time observer can converge in finite time, facilitating rapid identification of MLOs. Numerical experiments demonstrate the algorithm's rapidity and scalability.
{"title":"Distributed Finite-Time Observer for Rapid Detection of Multiple Line Outages in Transmission Networks With Uncertain Parameters","authors":"Yu Chen;Zhi-Wei Liu;Guanghui Wen;Yan-Wu Wang","doi":"10.1109/TCNS.2024.3510364","DOIUrl":"https://doi.org/10.1109/TCNS.2024.3510364","url":null,"abstract":"Fast line outages detection is crucial for the safe operation of power systems. This article proposes a novel algorithm for detecting multiple line outages (MLOs) using a distributed finite-time observer, which overcomes the limitations of existing methods that rely on difficult-to-obtain information, such as rotor inertia and damping ratio. The proposed algorithm relies solely on local measurements and information from neighbors to update each node's local observer. Rigorous mathematical analysis validates that the finite-time observer can converge in finite time, facilitating rapid identification of MLOs. Numerical experiments demonstrate the algorithm's rapidity and scalability.","PeriodicalId":56023,"journal":{"name":"IEEE Transactions on Control of Network Systems","volume":"12 1","pages":"596-608"},"PeriodicalIF":4.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688082","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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