IEEE Control Systems Letters最新文献_第4页

A Robust Human-Autonomy Collaboration Framework With Experimental Validation 通过实验验证的稳健的人机协作框架

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-09-24 DOI: 10.1109/LCSYS.2024.3467188

M. Yusuf Uzun;Emirhan Inanc;Yildiray Yildiz

引用次数: 0

A System Fault Diagnosis Method Based on Labeled Time Petri Net With Data 基于带数据标签时间 Petri 网的系统故障诊断方法

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-09-23 DOI: 10.1109/LCSYS.2024.3466515

Jian Song;Guanjun Liu

{"title":"A System Fault Diagnosis Method Based on Labeled Time Petri Net With Data","authors":"Jian Song;Guanjun Liu","doi":"10.1109/LCSYS.2024.3466515","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3466515","url":null,"abstract":"Regarding the fault diagnosis issue in real-time discrete event systems, existing detection methods typically involve modeling using labeled time Petri net (LTPN), followed by using the generated modified state class graph (MSCG) from LTPN to detect faults in the system. The diagnosis results are classified into normal, uncertain, and faulty. However, existing fault detection methods are based on time sequences for diagnosis; they ignore the changes in data flow in the system, making it impossible to determine whether there is a fault in the system when the detection result is uncertain. To address this issue, this letter proposes a modeling method of LTPN with data (LTPND), which binds corresponding data element operations on the transitions of LTPN to simulate the data flow in the system. Subsequently, the MSCG of LTPND is constructed, and a corresponding fault diagnosis algorithm is proposed based on MSCG. By traversing all the paths that satisfy the fire time and sequence information of observable transitions, faults in the system are detected from the perspectives of time constraints and data element changes, thus accurately judging the specific situations of uncertain states. Finally, the feasibility and effectiveness of the proposed method are validated through case analysis.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2337-2342"},"PeriodicalIF":2.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Novel Sampled-Output Feedback Controller Guaranteeing Lyapunov Stability for Continuous-Time Uncertain Nonautonomous Nonlinear Systems 保证连续时间不确定非自主非线性系统 Lyapunov 稳定性的新型采样输出反馈控制器

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-09-19 DOI: 10.1109/LCSYS.2024.3464331

Jang-Hyun Park

引用次数: 0

Dynamic Tube Control for DC Microgrids 直流微电网的动态磁管控制

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-09-19 DOI: 10.1109/LCSYS.2024.3464329

Grigoris Michos;George C. Konstantopoulos;Paul A. Trodden

引用次数: 0

On a Discrete-Time Networked Epidemic Model With Time-Varying Heterogeneous Delays 关于时变异质延迟的离散时间网络流行病模型

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-09-18 DOI: 10.1109/LCSYS.2024.3463499

Fangzhou Liu;Lei Shi;Jinliang Shao;Qingchen Liu

引用次数: 0

Byzantine Attack Resilient Bounded Consensus 拜占庭攻击弹性有界共识

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-09-11 DOI: 10.1109/LCSYS.2024.3457851

Jinxin Miao;Zhenqian Wang;Xiwang Dong;Zhiqiang Zuo;Jinhu Lü

{"title":"Byzantine Attack Resilient Bounded Consensus","authors":"Jinxin Miao;Zhenqian Wang;Xiwang Dong;Zhiqiang Zuo;Jinhu Lü","doi":"10.1109/LCSYS.2024.3457851","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3457851","url":null,"abstract":"Average consensus is a cornerstone of distributed systems, facilitating essential functionalities such as distributed information fusion, decision-making, and decentralized control. However, achieving the exact average consensus is challenging when partial nodes are compromised and act as Byzantine attackers by transmitting malicious messages using judiciously crafted patterns. Existing resilient consensus results can only guarantee that the consensus value under Byzantine attacks remains within the range defined by the maximum and minimum initial state values of all legitimate nodes. In addition, the consensus value is highly uncertain when attack strategies change. In this letter, we propose a new resilient consensus algorithm that ensures the consensus value falls within a much tighter bound. The bound contains the exact average consensus value and is solely determined by the initial states of legitimate nodes, regardless of the attack strategies employed by the Byzantine attackers. More interestingly, we demonstrate that the bound is the tightest achievable under our resilient consensus algorithm when the number of Byzantine attackers reaches the maximum threshold our algorithm can handle. Numerical simulations are given to validate the theoretical results.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2211-2216"},"PeriodicalIF":2.4,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Functional Diagnosability of Possibly Uncertain Systems 可能不确定系统的功能可诊断性

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-09-09 DOI: 10.1109/LCSYS.2024.3456230

Carine Jauberthie;Nathalie Verdière;Louise Travé-Massuyès

{"title":"Functional Diagnosability of Possibly Uncertain Systems","authors":"Carine Jauberthie;Nathalie Verdière;Louise Travé-Massuyès","doi":"10.1109/LCSYS.2024.3456230","DOIUrl":"https://doi.org/10.1109/LCSYS.2024.3456230","url":null,"abstract":"Diagnosability is a crucial attribute of systems and their instrumentation, ensuring that specified faults can be uniquely identified using the available sensors. In a model-based context, diagnosability is evaluated through analytical redundancy relations derived from the model by eliminating unknown variables. These relations, evaluated from sensor data, yield residuals, which indicate the system’s normal or faulty state. Ideally, residuals exhibit distinct values for different faults, generating unique fault signatures that facilitate fault discrimination and affirm system diagnosability. This letter presents a sufficient condition for the functional diagnosability of nonlinear dynamical systems, based on the functional linear independence of fault signatures. Unlike conventional diagnosability analysis, which focuses on residuals evaluated in a binary manner, 0 when not sensitive to a fault and 1 otherwise, functional diagnosability emphasizes the system’s behavior by evaluating the functional expressions of residuals defined as functional fault signatures. Evaluated from sensor data, functional signatures allow for an analysis of the whole residual trajectories. This advantageously increases the discriminating power. This approach leverages the symbolic framework of differential algebra, accommodating both deterministic and bounded uncertain systems without the need for a set-membership framework.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"8 ","pages":"2181-2186"},"PeriodicalIF":2.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Sensor Marginalized Particle Filtering for Dynamic Source Estimation 用于动态源估计的多传感器边际粒子滤波技术

IF 2.4

IEEE Control Systems Letters Pub Date : 2024-09-05 DOI: 10.1109/LCSYS.2024.3455248

Nicola Forti;Giorgio Battistelli;Luigi Chisci