Pub Date : 2024-09-30DOI: 10.1109/TSMC.2024.3462469
Lien-Wu Chen;Chih-Cheng Tsao
In this article, we propose a time-dependent lane-level navigation (TDLN) framework with spatiotemporal mobility modeling based on the Internet of Vehicles (IoV). The proposed TDLN framework can provide drivers with the fastest navigation path that can avoid passing congestion areas and predict vehicle spatiotemporal mobility of future traffic flows by estimating the travel time of road segments and the waiting time of intersections. According to our review of relevant research, TDLN is the first lane-level navigation solution that can provide the following features: 1) it can navigate vehicles in a lane-level manner and classify the queuing state of each vehicle as passing through an intersection; 2) it can estimate the driving time of lanes and the stopping time of intersections in different lanes to calculate the total delay time of passing through each lane and intersection; and 3) it can predict future traffic flows to determine the congestion level of each lane and explore predicted flow conditions on the road network to achieve the fastest navigation path planning. Simulation results show that TDLN outperforms existing methods and can plan the lane-level navigation path with the shortest travel time.
{"title":"Time-Dependent Lane-Level Navigation With Spatiotemporal Mobility Modeling Based on the Internet of Vehicles","authors":"Lien-Wu Chen;Chih-Cheng Tsao","doi":"10.1109/TSMC.2024.3462469","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3462469","url":null,"abstract":"In this article, we propose a time-dependent lane-level navigation (TDLN) framework with spatiotemporal mobility modeling based on the Internet of Vehicles (IoV). The proposed TDLN framework can provide drivers with the fastest navigation path that can avoid passing congestion areas and predict vehicle spatiotemporal mobility of future traffic flows by estimating the travel time of road segments and the waiting time of intersections. According to our review of relevant research, TDLN is the first lane-level navigation solution that can provide the following features: 1) it can navigate vehicles in a lane-level manner and classify the queuing state of each vehicle as passing through an intersection; 2) it can estimate the driving time of lanes and the stopping time of intersections in different lanes to calculate the total delay time of passing through each lane and intersection; and 3) it can predict future traffic flows to determine the congestion level of each lane and explore predicted flow conditions on the road network to achieve the fastest navigation path planning. Simulation results show that TDLN outperforms existing methods and can plan the lane-level navigation path with the shortest travel time.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7721-7732"},"PeriodicalIF":8.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672072","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 : 2024-09-27DOI: 10.1109/TSMC.2024.3460749
Xiaowei Yu;Xiaoli Li
In this article, a global output feedback control scheme is developed for a class of uncertain nonlinear systems subject to input quantization and unknown output function. By employing a time-varying gain and a time-invariant gain, we address the challenges posed by quantization errors and nonlinear functions with an unknown linear growth rate. Additionally, we determine an allowable measurement sensitivity error by solving a straightforward inequality. We demonstrate that the proposed scheme ensures global asymptotic stability for the system and guarantees that all signals of the closed-loop system remain bounded. Finally, we validate the proposed approach through a mathematical example and an experiment conducted on the QUBE-Servo 2 equipped with an inertial disc module.
{"title":"Global Asymptotic Stabilization Control for Uncertain Nonlinear Systems With Input Quantization and Unknown Output Function","authors":"Xiaowei Yu;Xiaoli Li","doi":"10.1109/TSMC.2024.3460749","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3460749","url":null,"abstract":"In this article, a global output feedback control scheme is developed for a class of uncertain nonlinear systems subject to input quantization and unknown output function. By employing a time-varying gain and a time-invariant gain, we address the challenges posed by quantization errors and nonlinear functions with an unknown linear growth rate. Additionally, we determine an allowable measurement sensitivity error by solving a straightforward inequality. We demonstrate that the proposed scheme ensures global asymptotic stability for the system and guarantees that all signals of the closed-loop system remain bounded. Finally, we validate the proposed approach through a mathematical example and an experiment conducted on the QUBE-Servo 2 equipped with an inertial disc module.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7528-7533"},"PeriodicalIF":8.6,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672074","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 : 2024-09-27DOI: 10.1109/TSMC.2024.3458939
Benyuan Yang;Hesuan Hu
There are two foundational problems in automated manufacturing systems. One is to determine their robustness (i.e., checking whether a marking is robust or nonrobust) while the other is to determine their liveness (i.e., determining whether a marking is live, bad, deadlock, or livelock). However, existing methods deal with them separately. This renders the existing methods inefficient in practice. In this article, we investigate the relation between robustness and liveness. First, we show how to define robustness in different net systems, i.e., the live, bounded, and nonreversible or reversible net systems. Second, we present a reachability graph-based method to assess the robustness of markings. Third, we clarify the relation between robustness and liveness, and conclude that liveness is a special case of robustness, under which the set of unreliable transitions is null. As a result, the robustness determination method developed in this article proves to be much general and can be used to check the liveness of each marking.
{"title":"On the Equivalence Between Robustness and Liveness in Automated Manufacturing Systems","authors":"Benyuan Yang;Hesuan Hu","doi":"10.1109/TSMC.2024.3458939","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3458939","url":null,"abstract":"There are two foundational problems in automated manufacturing systems. One is to determine their robustness (i.e., checking whether a marking is robust or nonrobust) while the other is to determine their liveness (i.e., determining whether a marking is live, bad, deadlock, or livelock). However, existing methods deal with them separately. This renders the existing methods inefficient in practice. In this article, we investigate the relation between robustness and liveness. First, we show how to define robustness in different net systems, i.e., the live, bounded, and nonreversible or reversible net systems. Second, we present a reachability graph-based method to assess the robustness of markings. Third, we clarify the relation between robustness and liveness, and conclude that liveness is a special case of robustness, under which the set of unreliable transitions is null. As a result, the robustness determination method developed in this article proves to be much general and can be used to check the liveness of each marking.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7495-7507"},"PeriodicalIF":8.6,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672075","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 : 2024-09-26DOI: 10.1109/TSMC.2024.3454541
Cyrus Hasanvand;Hamid Hasanvand;Hamidreza Momeni
Online intelligent knowledge extraction from real-world nonstationary data streams presents a multiobjective optimization challenge. Here, we characterize the learning process on a trajectory of global optimality to simultaneously satisfy six high-profile objectives: 1) optimum generalization for the best bias-variance tradeoff; 2) compactness of knowledgebase; 3) memory retention and stability-plasticity balance; 4) universality and full autonomy; 5) robustness against outliers, noise, and model uncertainty; and 6) active concept drift detection and adaptation. We propose a flexible Takagi-Sugeno (TS) fuzzy system, named UFAREX, that self-constructs and self-guards from scratch in a non-iterative sample-wise training scheme without storing data. Through quantification of various uncertainties, an adaptive prediction interval (API) is sequentially learned for each local dynamism to automatically capture the most accurate compact representation with a 95% confidence. This leads to the best linear unbiased estimation (BLUE) of local trends. To avoid catastrophic forgetting, API collaborates with trapezoidal membership functions (TMFs) to expand local boundaries with maximum plasticity and without distortive extrapolation. As a robust detection mechanism, API also pinpoints regions in conflict (RIC) where concept drifts and outliers are actively expressed w.r.t. time of occurrence, location, type, and severity. This establishes a single-sample online active concept drift management with zero buffer latency for regression applications. No heuristic forgetting, pruning, splitting, merging, and weighting mechanisms are exercised to prevent human intervention and render universality. UFAREX was comparatively tested on four real-world benchmarks. It stands out as an autonomous system geared for adaptive modeling, time-series forecasting, anomaly monitoring, and robust fault detection and diagnosis.
{"title":"UFAREX: A Universal Fully Autonomous Robust Expansionist Fuzzy System for Optimal Online Learning From Nonstationary Data Streams","authors":"Cyrus Hasanvand;Hamid Hasanvand;Hamidreza Momeni","doi":"10.1109/TSMC.2024.3454541","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3454541","url":null,"abstract":"Online intelligent knowledge extraction from real-world nonstationary data streams presents a multiobjective optimization challenge. Here, we characterize the learning process on a trajectory of global optimality to simultaneously satisfy six high-profile objectives: 1) optimum generalization for the best bias-variance tradeoff; 2) compactness of knowledgebase; 3) memory retention and stability-plasticity balance; 4) universality and full autonomy; 5) robustness against outliers, noise, and model uncertainty; and 6) active concept drift detection and adaptation. We propose a flexible Takagi-Sugeno (TS) fuzzy system, named UFAREX, that self-constructs and self-guards from scratch in a non-iterative sample-wise training scheme without storing data. Through quantification of various uncertainties, an adaptive prediction interval (API) is sequentially learned for each local dynamism to automatically capture the most accurate compact representation with a 95% confidence. This leads to the best linear unbiased estimation (BLUE) of local trends. To avoid catastrophic forgetting, API collaborates with trapezoidal membership functions (TMFs) to expand local boundaries with maximum plasticity and without distortive extrapolation. As a robust detection mechanism, API also pinpoints regions in conflict (RIC) where concept drifts and outliers are actively expressed w.r.t. time of occurrence, location, type, and severity. This establishes a single-sample online active concept drift management with zero buffer latency for regression applications. No heuristic forgetting, pruning, splitting, merging, and weighting mechanisms are exercised to prevent human intervention and render universality. UFAREX was comparatively tested on four real-world benchmarks. It stands out as an autonomous system geared for adaptive modeling, time-series forecasting, anomaly monitoring, and robust fault detection and diagnosis.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7419-7433"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672150","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 : 2024-09-26DOI: 10.1109/TSMC.2024.3453549
Feng Li;Yaokai Hu;Huisheng Zhang;Ansheng Deng;Jacek M. Zurada
Group regularization is commonly employed in network pruning to achieve structured model compression. However, the rationale behind existing studies on group regularization predominantly hinges on the sparsity capabilities of $L_{p}$ regularizers. This singular focus may lead to erroneous interpretations. In response to these limitations, this article proposes a novel framework for evaluating the penalization efficacy of group regularization methods by analyzing the impact of $L_{p}$ regularizers on weight magnitudes and weight group magnitudes. Within this framework, we demonstrate that $L_{1,2}$ regularization, contrary to prevailing literature, indeed exhibits favorable performance in structured pruning tasks. Motivated by this insight, we introduce a hybrid group regularization approach that integrates $L_{1,2}$ regularization and group $L_{1/2}$ regularization (denoted as HGL1,2& $L_{1/2}$ ). This novel method addresses the challenge of selecting appropriate $L_{p}$ regularizers for penalizing weight groups by leveraging $L_{1,2}$ regularization for penalizing groups with magnitudes exceeding a critical threshold while employing group $L_{1/2}$ regularization for other groups. Experimental evaluations are conducted to verify the efficiency of the proposed hybrid group regularization method and the viability of the introduced framework.
{"title":"A Group Regularization Framework of Convolutional Neural Networks Based on the Impact of Lₚ Regularizers on Magnitude","authors":"Feng Li;Yaokai Hu;Huisheng Zhang;Ansheng Deng;Jacek M. Zurada","doi":"10.1109/TSMC.2024.3453549","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3453549","url":null,"abstract":"Group regularization is commonly employed in network pruning to achieve structured model compression. However, the rationale behind existing studies on group regularization predominantly hinges on the sparsity capabilities of \u0000<inline-formula> <tex-math>$L_{p}$ </tex-math></inline-formula>\u0000 regularizers. This singular focus may lead to erroneous interpretations. In response to these limitations, this article proposes a novel framework for evaluating the penalization efficacy of group regularization methods by analyzing the impact of \u0000<inline-formula> <tex-math>$L_{p}$ </tex-math></inline-formula>\u0000 regularizers on weight magnitudes and weight group magnitudes. Within this framework, we demonstrate that \u0000<inline-formula> <tex-math>$L_{1,2}$ </tex-math></inline-formula>\u0000 regularization, contrary to prevailing literature, indeed exhibits favorable performance in structured pruning tasks. Motivated by this insight, we introduce a hybrid group regularization approach that integrates \u0000<inline-formula> <tex-math>$L_{1,2}$ </tex-math></inline-formula>\u0000 regularization and group \u0000<inline-formula> <tex-math>$L_{1/2}$ </tex-math></inline-formula>\u0000 regularization (denoted as HGL1,2&\u0000<inline-formula> <tex-math>$L_{1/2}$ </tex-math></inline-formula>\u0000). This novel method addresses the challenge of selecting appropriate \u0000<inline-formula> <tex-math>$L_{p}$ </tex-math></inline-formula>\u0000 regularizers for penalizing weight groups by leveraging \u0000<inline-formula> <tex-math>$L_{1,2}$ </tex-math></inline-formula>\u0000 regularization for penalizing groups with magnitudes exceeding a critical threshold while employing group \u0000<inline-formula> <tex-math>$L_{1/2}$ </tex-math></inline-formula>\u0000 regularization for other groups. Experimental evaluations are conducted to verify the efficiency of the proposed hybrid group regularization method and the viability of the introduced framework.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7434-7444"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672147","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 : 2024-09-26DOI: 10.1109/TSMC.2024.3460369
Kunting Yu;Yongming Li;Maolong Lv;Shaocheng Tong
This article introduces an adaptive fuzzy control methodology employing dynamic event-triggered communication for underactuated multiple unmanned surface vehicles (USVs) with modeling uncertainties. The key innovations of the proposed formation control strategy can be summarized as follows: 1) each USV is equipped with a dynamic event-triggered mechanism, ensuring that the controller and neighboring USVs receive position and yaw angle information only when this mechanism is triggered, enhancing communication efficiency; 2) distributed filters are implemented to continuous the event-triggered information; and 3) by employing the fuzzy logical systems (FLSs) to identify the unknown modeling uncertainties, local observers are designed to estimate unavailable velocity and yaw rate. Based on the dynamic event-triggered mechanism, distributed filters and local observers, a nondifferentiable-free backstepping procedure is proposed. The closed-loop stability is proven through Lyapunov stability theory, and Zeno behavior of the dynamic event-triggered mechanism is demonstrated through reductio. Simulation results are presented to validate the effectiveness of the proposed control strategy.
{"title":"Adaptive Fuzzy Formation Control for Underactuated Multi-USVs With Dynamic Event-Triggered Communication","authors":"Kunting Yu;Yongming Li;Maolong Lv;Shaocheng Tong","doi":"10.1109/TSMC.2024.3460369","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3460369","url":null,"abstract":"This article introduces an adaptive fuzzy control methodology employing dynamic event-triggered communication for underactuated multiple unmanned surface vehicles (USVs) with modeling uncertainties. The key innovations of the proposed formation control strategy can be summarized as follows: 1) each USV is equipped with a dynamic event-triggered mechanism, ensuring that the controller and neighboring USVs receive position and yaw angle information only when this mechanism is triggered, enhancing communication efficiency; 2) distributed filters are implemented to continuous the event-triggered information; and 3) by employing the fuzzy logical systems (FLSs) to identify the unknown modeling uncertainties, local observers are designed to estimate unavailable velocity and yaw rate. Based on the dynamic event-triggered mechanism, distributed filters and local observers, a nondifferentiable-free backstepping procedure is proposed. The closed-loop stability is proven through Lyapunov stability theory, and Zeno behavior of the dynamic event-triggered mechanism is demonstrated through reductio. Simulation results are presented to validate the effectiveness of the proposed control strategy.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7783-7793"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672172","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}
Electroencephalogram (EEG) brain network embodies the brain’s coordination and interaction mechanism, and the transformations of emotional states are usually accompanied with changes in brain network spatial topologies. To effectively characterize emotions, in this work, we propose a cognition-inspired graph embedding model in the L1-norm space (L1-CGE) to learn an optimal low-dimensional embedded manifold for emotional brain networks. In the L1-CGE, the original brain networks are first encoded in the affinity space with the proposed cognition-inspired metric to construct the latent geometry manifold structure of emotional brain networks, and then the graph learning objective function is defined in the L1-norm space to obtain the optimal low-dimensional representations of brain networks. Essentially, the modularized community structures of emotional brain networks can be effectively emphasized by the L1-CGE to realize an effective depiction for emotions. Compared with existing methods, the L1-CGE model has achieved state-of-the-art performance on three public emotional EEG datasets in off-line conditions. Besides, the robust real-time experimental results have been achieved with the on-line emotion decoding system designed with L1-CGE. Both off- and on-line experimental results consistently demonstrate that the proposed L1-CGE is promising to provide a potential solution for the real-time affective brain-computer interface (aBCI) system.
{"title":"Brain Network Manifold Learned by Cognition-Inspired Graph Embedding Model for Emotion Recognition","authors":"Cunbo Li;Peiyang Li;Zhaojin Chen;Lei Yang;Fali Li;Feng Wan;Zehong Cao;Dezhong Yao;Bao-Liang Lu;Peng Xu","doi":"10.1109/TSMC.2024.3458949","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3458949","url":null,"abstract":"Electroencephalogram (EEG) brain network embodies the brain’s coordination and interaction mechanism, and the transformations of emotional states are usually accompanied with changes in brain network spatial topologies. To effectively characterize emotions, in this work, we propose a cognition-inspired graph embedding model in the L1-norm space (L1-CGE) to learn an optimal low-dimensional embedded manifold for emotional brain networks. In the L1-CGE, the original brain networks are first encoded in the affinity space with the proposed cognition-inspired metric to construct the latent geometry manifold structure of emotional brain networks, and then the graph learning objective function is defined in the L1-norm space to obtain the optimal low-dimensional representations of brain networks. Essentially, the modularized community structures of emotional brain networks can be effectively emphasized by the L1-CGE to realize an effective depiction for emotions. Compared with existing methods, the L1-CGE model has achieved state-of-the-art performance on three public emotional EEG datasets in off-line conditions. Besides, the robust real-time experimental results have been achieved with the on-line emotion decoding system designed with L1-CGE. Both off- and on-line experimental results consistently demonstrate that the proposed L1-CGE is promising to provide a potential solution for the real-time affective brain-computer interface (aBCI) system.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7794-7808"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672070","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}
This article investigates the distributed constrained optimization problem with event-triggered communication over time-varying weight-unbalanced directed graphs. A more generalized network model is considered where the communication topology may be variable and unbalanced over time, the information flows across agents are subject to time-varying communication delays, and agents are not required to know their out-degree information accurately. To address the above challenges, we propose a novel discrete-time distributed event-triggered delay subgradient algorithm. To facilitate convergence analysis, a consensus-only “virtual” agent technique is employed, dynamically adjusting its state (active or asleep) to ensure a delay-free information flow among agents. Additionally, an augmentation approach is proposed to ensure that the augmented time-varying weight matrix is row-stochastic. It is shown that the agents’ local decision variables converge to the same optimal solution, in the case of reasonable communication delays and event-triggering thresholds. Numerical examples show the efficiency of the proposed algorithm.
{"title":"An Event-Based Delayed Projection Row-Stochastic Method for Distributed Constrained Optimization Over Time-Varying Graphs","authors":"Mingqi Xing;Dazhong Ma;Huaguang Zhang;Xiangpeng Xie","doi":"10.1109/TSMC.2024.3458972","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3458972","url":null,"abstract":"This article investigates the distributed constrained optimization problem with event-triggered communication over time-varying weight-unbalanced directed graphs. A more generalized network model is considered where the communication topology may be variable and unbalanced over time, the information flows across agents are subject to time-varying communication delays, and agents are not required to know their out-degree information accurately. To address the above challenges, we propose a novel discrete-time distributed event-triggered delay subgradient algorithm. To facilitate convergence analysis, a consensus-only “virtual” agent technique is employed, dynamically adjusting its state (active or asleep) to ensure a delay-free information flow among agents. Additionally, an augmentation approach is proposed to ensure that the augmented time-varying weight matrix is row-stochastic. It is shown that the agents’ local decision variables converge to the same optimal solution, in the case of reasonable communication delays and event-triggering thresholds. Numerical examples show the efficiency of the proposed algorithm.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7508-7520"},"PeriodicalIF":8.6,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672117","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 : 2024-09-25DOI: 10.1109/TSMC.2024.3462728
Panagiotis S. Trakas;Charalampos P. Bechlioulis
In this work, we propose an approximation-free adaptive performance control scheme for unknown high-relative degree, multi-input-multioutput (MIMO) nonlinear systems with saturation on the control input signal. We introduce a novel reconciling adaptive modification of the predefined performance specifications based on the input constraints of the controlled plant, providing the best-feasible output performance. The automatic gain tuning in combination with the simplicity of the proposed controller enhance its robustness and enable its easy deployment in practical scenarios. Notably, the introduced control methodology ensures the necessary compromise between input-output constraints on a semi-global sense for ISS systems. However, the stability attributes for general nonlinear systems are inevitably limited to compact domains due to the inherent conflict between performance demand and actuation capability. In this context, we provide a sufficient closed-loop stability criterion through Lyapunov analysis. Finally, illustrative simulation studies and experimental results clarify and verify the efficacy of the proposed controller.
在这项研究中，我们针对控制输入信号饱和的未知高相对度、多输入多输出（MIMO）非线性系统提出了一种无近似自适应性能控制方案。我们根据受控工厂的输入约束条件，引入了一种新颖的调和自适应修改预定义性能指标的方法，以提供最佳可行的输出性能。自动增益调整与拟议控制器的简易性相结合，增强了控制器的鲁棒性，使其能够轻松应用于实际场景。值得注意的是，引入的控制方法确保了 ISS 系统在半全局意义上的输入输出约束之间的必要折衷。然而，由于性能需求与执行能力之间的内在冲突，一般非线性系统的稳定性属性不可避免地局限于紧凑域。在这种情况下，我们通过 Lyapunov 分析提供了充分的闭环稳定性标准。最后，说明性仿真研究和实验结果澄清并验证了所提控制器的功效。
{"title":"Adaptive Performance Control for Input Constrained MIMO Nonlinear Systems","authors":"Panagiotis S. Trakas;Charalampos P. Bechlioulis","doi":"10.1109/TSMC.2024.3462728","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3462728","url":null,"abstract":"In this work, we propose an approximation-free adaptive performance control scheme for unknown high-relative degree, multi-input-multioutput (MIMO) nonlinear systems with saturation on the control input signal. We introduce a novel reconciling adaptive modification of the predefined performance specifications based on the input constraints of the controlled plant, providing the best-feasible output performance. The automatic gain tuning in combination with the simplicity of the proposed controller enhance its robustness and enable its easy deployment in practical scenarios. Notably, the introduced control methodology ensures the necessary compromise between input-output constraints on a semi-global sense for ISS systems. However, the stability attributes for general nonlinear systems are inevitably limited to compact domains due to the inherent conflict between performance demand and actuation capability. In this context, we provide a sufficient closed-loop stability criterion through Lyapunov analysis. Finally, illustrative simulation studies and experimental results clarify and verify the efficacy of the proposed controller.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7733-7745"},"PeriodicalIF":8.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693631","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672143","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 : 2024-09-23DOI: 10.1109/TSMC.2024.3456810
Ahmad Mohammad Saber;Amr Youssef;Davor Svetinovic;Hatem Zeineldin;Ehab F. El-Saadany
Line current differential relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-masking attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this article, we propose a two-module framework to detect FMAs. The first module is a mismatch index (MI) developed from the protected transmission line’s equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR’s local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT’s real-time simulator confirm the proposed solution’s real-time performance capability.
{"title":"Unmasking Covert Intrusions: Detection of Fault-Masking Cyberattacks on Differential Protection Systems","authors":"Ahmad Mohammad Saber;Amr Youssef;Davor Svetinovic;Hatem Zeineldin;Ehab F. El-Saadany","doi":"10.1109/TSMC.2024.3456810","DOIUrl":"https://doi.org/10.1109/TSMC.2024.3456810","url":null,"abstract":"Line current differential relays (LCDRs) are high-speed relays progressively used to protect critical transmission lines. However, LCDRs are vulnerable to cyberattacks. Fault-masking attacks (FMAs) are stealthy cyberattacks performed by manipulating the remote measurements of the targeted LCDR to disguise faults on the protected line. Hence, they remain undetected by this LCDR. In this article, we propose a two-module framework to detect FMAs. The first module is a mismatch index (MI) developed from the protected transmission line’s equivalent physical model. The MI is triggered only if there is a significant mismatch in the LCDR’s local and remote measurements while the LCDR itself is untriggered, which indicates an FMA. After the MI is triggered, the second module, a neural network-based classifier, promptly confirms that the triggering event is a physical fault that lies on the line protected by the LCDR before declaring the occurrence of an FMA. The proposed framework is tested using the IEEE 39-bus benchmark system. Our simulation results confirm that the proposed framework can accurately detect FMAs on LCDRs and is not affected by normal system disturbances, variations, or measurement noise. Our experimental results using OPAL-RT’s real-time simulator confirm the proposed solution’s real-time performance capability.","PeriodicalId":48915,"journal":{"name":"IEEE Transactions on Systems Man Cybernetics-Systems","volume":"54 12","pages":"7683-7696"},"PeriodicalIF":8.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672025","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}
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