Pub Date : 2025-02-05DOI: 10.1109/TCYB.2025.3527862
A. Urio-Larrea;H. Camargo;G. Lucca;T. Asmus;C. Marco-Detchart;L. Schick;C. Lopez-Molina;J. Andreu-Perez;H. Bustince;G. P. Dimuro
In data stream (DS) learning, the system has to extract knowledge from data generated continuously, usually at high speed and in large volumes, making it impossible to store the entire set of data to be processed in batch mode. Hence, machine learning models must be built incrementally by processing the incoming examples, as data arrive, while updating the model to be compatible with the current data. In fuzzy DS clustering, the model can either absorb incoming data into existing clusters or initiate a new cluster. As the volume of data increases, there is a possibility that the clusters will overlap to the point where it is convenient to merge two or more clusters into one. Then, a cluster comparison measure (CM) should be applied, to decide whether such clusters should be combined, also in an incremental manner. This defines an incremental fusion process based on aggregation functions that can aggregate the incoming inputs without storing all the previous inputs. The objective of this article is to solve the fuzzy DS clustering problem of incrementally comparing fuzzy clusters on a formal basis. First, we formalize and operationalize incremental fusion processes of fuzzy clusters by introducing recursively extendable (RE) aggregation functions, studying construction methods and different classes of such functions. Second, we propose two approaches to compare clusters: 1) similarity and 2) overlapping between clusters, based on RE aggregation functions. Finally, we analyze the effect of those incremental CMs on the online and offline phases of the well-known fuzzy clustering algorithm d-FuzzStream, showing that our new approach outperforms the original algorithm and presents better or comparable performance to other state-of-the-art DS clustering algorithms found in the literature.
{"title":"Data Stream Clustering: Introducing Recursively Extendable Aggregation Functions for Incremental Cluster Fusion Processes","authors":"A. Urio-Larrea;H. Camargo;G. Lucca;T. Asmus;C. Marco-Detchart;L. Schick;C. Lopez-Molina;J. Andreu-Perez;H. Bustince;G. P. Dimuro","doi":"10.1109/TCYB.2025.3527862","DOIUrl":"10.1109/TCYB.2025.3527862","url":null,"abstract":"In data stream (DS) learning, the system has to extract knowledge from data generated continuously, usually at high speed and in large volumes, making it impossible to store the entire set of data to be processed in batch mode. Hence, machine learning models must be built incrementally by processing the incoming examples, as data arrive, while updating the model to be compatible with the current data. In fuzzy DS clustering, the model can either absorb incoming data into existing clusters or initiate a new cluster. As the volume of data increases, there is a possibility that the clusters will overlap to the point where it is convenient to merge two or more clusters into one. Then, a cluster comparison measure (CM) should be applied, to decide whether such clusters should be combined, also in an incremental manner. This defines an incremental fusion process based on aggregation functions that can aggregate the incoming inputs without storing all the previous inputs. The objective of this article is to solve the fuzzy DS clustering problem of incrementally comparing fuzzy clusters on a formal basis. First, we formalize and operationalize incremental fusion processes of fuzzy clusters by introducing recursively extendable (RE) aggregation functions, studying construction methods and different classes of such functions. Second, we propose two approaches to compare clusters: 1) similarity and 2) overlapping between clusters, based on RE aggregation functions. Finally, we analyze the effect of those incremental CMs on the online and offline phases of the well-known fuzzy clustering algorithm d-FuzzStream, showing that our new approach outperforms the original algorithm and presents better or comparable performance to other state-of-the-art DS clustering algorithms found in the literature.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1421-1435"},"PeriodicalIF":9.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-05DOI: 10.1109/TCYB.2025.3531494
Changdong Wang;Zhou Shu;Jingli Yang;Zhenyu Zhao;Huamin Jie;Yongqi Chang;Shiqi Jiang;Kye Yak See
To alleviate data distribution under different operating conditions, domain generalization (DG) has been applied in mechanical diagnosis. Still, its effectiveness is limited when unknown fault states appear in the target domain. Consequently, open set DG (OSDG) has emerged to identify unknown classes in unknown domains. However, data collection costs and safety concerns have resulted in a significant class imbalance in OSDG. This imbalance causes the decision boundary to be skewed toward abundant positive classes, ultimately leading to misclassifying unknown states and increasing security risks. Currently, there is a lack of methods to simultaneously address domain shift and class shift in an imbalanced unknown domain. To tackle this issue, this article proposes a multisource domain-class gradient coordination meta-learning (MDGCML) framework, which can learn the generalized boundaries of all tasks by coordinating gradients between interdomains and interclasses. Based on the MDGCML, a joint learning paradigm involving the sharing of parameters between open-set classifiers and closed-set classifiers is constructed to enable quick adaption of the model to unknown domains. The superior performance of the proposed framework has been verified on two datasets.
{"title":"Learning to Imbalanced Open Set Generalize: A Meta-Learning Framework for Enhanced Mechanical Diagnosis","authors":"Changdong Wang;Zhou Shu;Jingli Yang;Zhenyu Zhao;Huamin Jie;Yongqi Chang;Shiqi Jiang;Kye Yak See","doi":"10.1109/TCYB.2025.3531494","DOIUrl":"10.1109/TCYB.2025.3531494","url":null,"abstract":"To alleviate data distribution under different operating conditions, domain generalization (DG) has been applied in mechanical diagnosis. Still, its effectiveness is limited when unknown fault states appear in the target domain. Consequently, open set DG (OSDG) has emerged to identify unknown classes in unknown domains. However, data collection costs and safety concerns have resulted in a significant class imbalance in OSDG. This imbalance causes the decision boundary to be skewed toward abundant positive classes, ultimately leading to misclassifying unknown states and increasing security risks. Currently, there is a lack of methods to simultaneously address domain shift and class shift in an imbalanced unknown domain. To tackle this issue, this article proposes a multisource domain-class gradient coordination meta-learning (MDGCML) framework, which can learn the generalized boundaries of all tasks by coordinating gradients between interdomains and interclasses. Based on the MDGCML, a joint learning paradigm involving the sharing of parameters between open-set classifiers and closed-set classifiers is constructed to enable quick adaption of the model to unknown domains. The superior performance of the proposed framework has been verified on two datasets.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1464-1475"},"PeriodicalIF":9.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-04DOI: 10.1109/TCYB.2025.3531657
Rui Liu;Yao Hu;Jibin Wu;Ka-Chun Wong;Zhi-An Huang;Yu-An Huang;Kay Chen Tan
Neuroimaging analysis aims to reveal the information-processing mechanisms of the human brain in a noninvasive manner. In the past, graph neural networks (GNNs) have shown promise in capturing the non-Euclidean structure of brain networks. However, existing neuroimaging studies focused primarily on spatial functional connectivity, despite temporal dynamics in complex brain networks. To address this gap, we propose a spatio-temporal interactive graph representation framework (STIGR) for dynamic neuroimaging analysis that encompasses different aspects from classification and regression tasks to interpretation tasks. STIGR leverages a dynamic adaptive-neighbor graph convolution network to capture the interrelationships between spatial and temporal dynamics. To address the limited global scope in graph convolutions, a self-attention module based on Transformers is introduced to extract long-term dependencies. Contrastive learning is used to adaptively contrast similarities between adjacent scanning windows, modeling cross-temporal correlations in dynamic graphs. Extensive experiments on six public neuroimaging datasets demonstrate the competitive performance of STIGR across different platforms, achieving state-of-the-art results in classification and regression tasks. The proposed framework enables the detection of remarkable temporal association patterns between regions of interest based on sequential neuroimaging signals, offering medical professionals a versatile and interpretable tool for exploring task-specific neurological patterns. Our codes and models are available at https://github.com/77YQ77/STIGR/.
{"title":"Dynamic Graph Representation Learning for Spatio-Temporal Neuroimaging Analysis","authors":"Rui Liu;Yao Hu;Jibin Wu;Ka-Chun Wong;Zhi-An Huang;Yu-An Huang;Kay Chen Tan","doi":"10.1109/TCYB.2025.3531657","DOIUrl":"10.1109/TCYB.2025.3531657","url":null,"abstract":"Neuroimaging analysis aims to reveal the information-processing mechanisms of the human brain in a noninvasive manner. In the past, graph neural networks (GNNs) have shown promise in capturing the non-Euclidean structure of brain networks. However, existing neuroimaging studies focused primarily on spatial functional connectivity, despite temporal dynamics in complex brain networks. To address this gap, we propose a spatio-temporal interactive graph representation framework (STIGR) for dynamic neuroimaging analysis that encompasses different aspects from classification and regression tasks to interpretation tasks. STIGR leverages a dynamic adaptive-neighbor graph convolution network to capture the interrelationships between spatial and temporal dynamics. To address the limited global scope in graph convolutions, a self-attention module based on Transformers is introduced to extract long-term dependencies. Contrastive learning is used to adaptively contrast similarities between adjacent scanning windows, modeling cross-temporal correlations in dynamic graphs. Extensive experiments on six public neuroimaging datasets demonstrate the competitive performance of STIGR across different platforms, achieving state-of-the-art results in classification and regression tasks. The proposed framework enables the detection of remarkable temporal association patterns between regions of interest based on sequential neuroimaging signals, offering medical professionals a versatile and interpretable tool for exploring task-specific neurological patterns. Our codes and models are available at <uri>https://github.com/77YQ77/STIGR/</uri>.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1121-1134"},"PeriodicalIF":9.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The innovation of optimal learning control methods is profoundly propelled due to the improvement of the learning ability. In this article, we investigate the synthesis of initialization and acceleration for optimal learning control algorithms. This approach contrasts with traditional methods that concentrate solely on either the improvement of initialization or acceleration. Specifically, we establish a novel relaxed policy iteration (PI) algorithm with self-learning horizon for stochastic optimal control. Notably, by suitably utilizing self-learning horizon, we can directly evaluate inadmissible policies to reduce the initialization burden. Meanwhile, the inadmissible policy can be rapidly optimized with few learning iterations. Then, several critical conclusions of relaxed optimal control are established by discussing algorithm convergence and system stability. Furthermore, to provide the convincing application potentials, a class of unconventional problems is effectively solved by the relaxed PI algorithm, including the dynamics with external noises and nonzero equilibrium. Finally, we present a series of nonlinear benchmarks with practical applications to comprehensively evaluate the performance of relaxed PI. The experimental results obtained from these diverse benchmarks uniformly highlight the effectiveness of self-learning horizon mechanism.
{"title":"Relaxed Optimal Control With Self-Learning Horizon for Discrete-Time Stochastic Dynamics","authors":"Ding Wang;Jiangyu Wang;Ao Liu;Derong Liu;Junfei Qiao","doi":"10.1109/TCYB.2025.3530951","DOIUrl":"10.1109/TCYB.2025.3530951","url":null,"abstract":"The innovation of optimal learning control methods is profoundly propelled due to the improvement of the learning ability. In this article, we investigate the synthesis of initialization and acceleration for optimal learning control algorithms. This approach contrasts with traditional methods that concentrate solely on either the improvement of initialization or acceleration. Specifically, we establish a novel relaxed policy iteration (PI) algorithm with self-learning horizon for stochastic optimal control. Notably, by suitably utilizing self-learning horizon, we can directly evaluate inadmissible policies to reduce the initialization burden. Meanwhile, the inadmissible policy can be rapidly optimized with few learning iterations. Then, several critical conclusions of relaxed optimal control are established by discussing algorithm convergence and system stability. Furthermore, to provide the convincing application potentials, a class of unconventional problems is effectively solved by the relaxed PI algorithm, including the dynamics with external noises and nonzero equilibrium. Finally, we present a series of nonlinear benchmarks with practical applications to comprehensively evaluate the performance of relaxed PI. The experimental results obtained from these diverse benchmarks uniformly highlight the effectiveness of self-learning horizon mechanism.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1183-1196"},"PeriodicalIF":9.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-04DOI: 10.1109/TCYB.2025.3531449
Jun Ma;Yong Zhang;Dun-Wei Gong;Xiao-Zhi Gao;Chao Peng
Constrained multiobjective optimization problems are widespread in practical engineering fields. Scholars have proposed various effective constrained multiobjective evolutionary algorithms (CMOEAs) for such problems. However, most existing algorithms overlook the differences between different decision variables in influencing the degree of constraint violation and still lack an effective handling mechanism for constraint-sensitive variables. To address this issue, a two-stage cooperation multiobjective evolutionary algorithm guided by constraint-sensitive variables (CV-TCMOEA) is proposed. In the first stage, a relatively simple auxiliary problem with only a few dominant constraints is constructed to approximate the original problem. After obtaining a set of approximate Pareto optimal solutions by dealing with the auxiliary problem, in the second stage, a constraint-sensitive variable-guided multistrategy cooperation search method is developed. In this method, decision variables are divided into two types: 1) constraint-sensitive and 2) constraint-insensitive variables, and a variable-type-guided cooperative individual update strategy is proposed to autonomously select appropriate search strategies for different types of variables. Experimental results on 28 benchmark functions and 10 engineering problems demonstrated the superiority of the CV-TCMOEA over seven state-of-the-art CMOEAs.
{"title":"Two-Stage Cooperation Multiobjective Evolutionary Algorithm Guided by Constraint-Sensitive Variables","authors":"Jun Ma;Yong Zhang;Dun-Wei Gong;Xiao-Zhi Gao;Chao Peng","doi":"10.1109/TCYB.2025.3531449","DOIUrl":"10.1109/TCYB.2025.3531449","url":null,"abstract":"Constrained multiobjective optimization problems are widespread in practical engineering fields. Scholars have proposed various effective constrained multiobjective evolutionary algorithms (CMOEAs) for such problems. However, most existing algorithms overlook the differences between different decision variables in influencing the degree of constraint violation and still lack an effective handling mechanism for constraint-sensitive variables. To address this issue, a two-stage cooperation multiobjective evolutionary algorithm guided by constraint-sensitive variables (CV-TCMOEA) is proposed. In the first stage, a relatively simple auxiliary problem with only a few dominant constraints is constructed to approximate the original problem. After obtaining a set of approximate Pareto optimal solutions by dealing with the auxiliary problem, in the second stage, a constraint-sensitive variable-guided multistrategy cooperation search method is developed. In this method, decision variables are divided into two types: 1) constraint-sensitive and 2) constraint-insensitive variables, and a variable-type-guided cooperative individual update strategy is proposed to autonomously select appropriate search strategies for different types of variables. Experimental results on 28 benchmark functions and 10 engineering problems demonstrated the superiority of the CV-TCMOEA over seven state-of-the-art CMOEAs.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1450-1463"},"PeriodicalIF":9.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-04DOI: 10.1109/TCYB.2025.3530456
Qinglai Wei;Hao Jiang
In this article, the optimal consensus tracking control for nonlinear multiagent systems (MASs) with unknown dynamics and disturbances is investigated via adaptive dynamic programming (ADP) technology. Taking into account the disturbance as control inputs, the optimal control problem for the nonlinear MASs is reformulated as a multiplayer zero-sum differential game. In addition, a single network ADP structure is constructed to approach the optimal consensus control policies. Subsequently, an event triggering mechanism is implemented to reduce the workload of the controller and conserve computing and communication resources. Since then, in order to further streamline the intricacies of controller design, this work is extended to self-triggered cases to alleviate the need for hardware devices to continuously monitor signals. By using the Lyapunov method, the stability of the nonlinear MASs and the uniform ultimate boundedness (UUB) of the weight estimation error of the critic neural network (NN) is proved. Finally, the simulation results for an MAS consisting of a single-link robot validate the effectiveness of the proposed control method.
{"title":"Event-/Self-Triggered Adaptive Optimal Consensus Control for Nonlinear Multiagent System With Unknown Dynamics and Disturbances","authors":"Qinglai Wei;Hao Jiang","doi":"10.1109/TCYB.2025.3530456","DOIUrl":"10.1109/TCYB.2025.3530456","url":null,"abstract":"In this article, the optimal consensus tracking control for nonlinear multiagent systems (MASs) with unknown dynamics and disturbances is investigated via adaptive dynamic programming (ADP) technology. Taking into account the disturbance as control inputs, the optimal control problem for the nonlinear MASs is reformulated as a multiplayer zero-sum differential game. In addition, a single network ADP structure is constructed to approach the optimal consensus control policies. Subsequently, an event triggering mechanism is implemented to reduce the workload of the controller and conserve computing and communication resources. Since then, in order to further streamline the intricacies of controller design, this work is extended to self-triggered cases to alleviate the need for hardware devices to continuously monitor signals. By using the Lyapunov method, the stability of the nonlinear MASs and the uniform ultimate boundedness (UUB) of the weight estimation error of the critic neural network (NN) is proved. Finally, the simulation results for an MAS consisting of a single-link robot validate the effectiveness of the proposed control method.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1476-1485"},"PeriodicalIF":9.4,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143125015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1109/TCYB.2025.3531181
{"title":"IEEE Transactions on Cybernetics","authors":"","doi":"10.1109/TCYB.2025.3531181","DOIUrl":"10.1109/TCYB.2025.3531181","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"C3-C3"},"PeriodicalIF":9.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2025-02-03DOI: 10.1109/TCYB.2025.3531183
{"title":"IEEE Transactions on Cybernetics","authors":"","doi":"10.1109/TCYB.2025.3531183","DOIUrl":"10.1109/TCYB.2025.3531183","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 2","pages":"C4-C4"},"PeriodicalIF":9.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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/TCYB.2025.3530561
Amedeo Andreotti;Bianca Caiazzo;Dario Giuseppe Lui;Alberto Petrillo;Stefania Santini
Since distributed control theory has now become a key ingredient in modern cyber-physical microgrids (MG), its implementation is inseparable from data communication. The introduction of a communication infrastructure inevitably brings communication threats, such as denial-of-service (DoS) attacks and network induced delays. This article represents the first attempt toward a unified distributed digital predictor-based control scheme for the solution of the secondary voltage restoration problem in islanded MGs, which also involves external unknown disturbances and network vulnerabilities, thus enhancing its resilience and reliability. The novel sampled-data predictive controller revises the conventional model reduction approach to reformulate it in a fully distributed digital way and involves some external disturbances information for prediction performance improvement, even though these perturbations are completely unknown. The main features of the resulting method are: 1) large delays compensation accounting for networked-induced delays and sleeping time interval due to DoS attacks occurrence and 2) unknown disturbance attenuation, usually neglected in the controllers synthesis phase in MGs field. Lyapunov-Krasovskii theory is exploited to analytically prove the exponential stability of the MG voltage, thus leading to linear matrix inequality-based sufficient stability conditions. Numerical and experimental results confirm theoretical derivations.
{"title":"Enhancing Resilience of Islanded Microgrids Under Disturbances, Delays, and DoS Attacks Through a Novel Digital Predictor Method","authors":"Amedeo Andreotti;Bianca Caiazzo;Dario Giuseppe Lui;Alberto Petrillo;Stefania Santini","doi":"10.1109/TCYB.2025.3530561","DOIUrl":"10.1109/TCYB.2025.3530561","url":null,"abstract":"Since distributed control theory has now become a key ingredient in modern cyber-physical microgrids (MG), its implementation is inseparable from data communication. The introduction of a communication infrastructure inevitably brings communication threats, such as denial-of-service (DoS) attacks and network induced delays. This article represents the first attempt toward a unified distributed digital predictor-based control scheme for the solution of the secondary voltage restoration problem in islanded MGs, which also involves external unknown disturbances and network vulnerabilities, thus enhancing its resilience and reliability. The novel sampled-data predictive controller revises the conventional model reduction approach to reformulate it in a fully distributed digital way and involves some external disturbances information for prediction performance improvement, even though these perturbations are completely unknown. The main features of the resulting method are: 1) large delays compensation accounting for networked-induced delays and sleeping time interval due to DoS attacks occurrence and 2) unknown disturbance attenuation, usually neglected in the controllers synthesis phase in MGs field. Lyapunov-Krasovskii theory is exploited to analytically prove the exponential stability of the MG voltage, thus leading to linear matrix inequality-based sufficient stability conditions. Numerical and experimental results confirm theoretical derivations.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 3","pages":"1310-1322"},"PeriodicalIF":9.4,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：