IEEE Transactions on Industrial Cyber-Physical Systems最新文献_第4页

Dual-Mode History-Dependent Attack Strategy Design for Cyber-Physical Systems 网络物理系统的双模历史相关攻击策略设计

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-03-05 DOI: 10.1109/TICPS.2026.3690587

Xiao-Lei Wang;Chen Peng;Dakui Wu

{"title":"Dual-Mode History-Dependent Attack Strategy Design for Cyber-Physical Systems","authors":"Xiao-Lei Wang;Chen Peng;Dakui Wu","doi":"10.1109/TICPS.2026.3690587","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3690587","url":null,"abstract":"This paper addresses the problem of designing a dual-mode history-dependent attack strategy for cyber-physical systems (CPSs) modeled by Markov jump systems (MJSs). A crucial challenge lies in the lack of dynamic adaptability in conventional attack strategies, which rely solely on single-time instant mode information, making it difficult to strike a balance between attack effectiveness and stealthiness. To tackle this issue, virtual systems are first established to quantify the attack impacts on asynchronous observers and attack detector, integrating multi-time instant mode evolution information of both the system and observer. Unlike the existing schemes, a dual-mode history-dependent attack strategy is proposed, which exploits historical statistical characteristics of modes to enhance robustness against complex jump behaviors. Furthermore, an <inline-formula><tex-math>$H_{-}/H_{infty }$</tex-math></inline-formula> optimization framework is introduced to maximize the attack's disruptive effect on the observer's estimation performance while minimizing the risk of detection. Finally, theoretical analysis and simulation results demonstrate that the proposed attack strategy achieves superior attack effectiveness and stealthiness compared to the traditional static attack models, thereby validating its practical applicability and advantages.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"314-323"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148003496","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

Secure Observer-Based Event-Triggered Control of Microgrid Load Frequency Control System Under DoS Attack DoS攻击下基于安全观测器的微电网负荷变频控制

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-07-28 DOI: 10.1109/TICPS.2026.3717534

Athira M. Mohan;Nader Meskin

{"title":"Secure Observer-Based Event-Triggered Control of Microgrid Load Frequency Control System Under DoS Attack","authors":"Athira M. Mohan;Nader Meskin","doi":"10.1109/TICPS.2026.3717534","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3717534","url":null,"abstract":"This study proposes a secondary remote observer-based DoS attack tolerant event-triggered control framework for an islanded microgrid load frequency control system with virtual inertia/auxiliary control subsystem while accounting for multiple practical challenges. In this work, a microgrid configuration with a remotely located secondary controller and state observer is considered, and an event-triggered communication mechanism is adopted to enhance communication efficiency in the secondary control loop. The secondary observer and controller gains adhering to a prescribed <inline-formula><tex-math>$H_{infty}$</tex-math></inline-formula> performance bound are derived leveraging Lyapunov-Krasovskii functional-based stability analysis and DoS attack tolerance is achieved using a modified event-triggering condition accounting DoS-induced extra output error. The stability conditions are derived by considering key system challenges, including the time-varying secondary measurement path transmission delay, event-triggering condition, remote implementation of the observer, and additional output error induced by DoS adversary. Finally, the efficacy of the proposed secondary control approach is demonstrated using Monte-Carlo simulation across different disturbance scenarios.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"771-782"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836551","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

Neural Network-Enhanced Super-Twisting Control for Industrial Robotic Manipulators via Time-Delay Estimation 基于时滞估计的神经网络增强工业机械臂超扭转控制

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-08-17 DOI: 10.1109/TICPS.2026.3724566

Yu-Zhu Xiang;Weiwei Yi;Sheng Li;Jian Guo;Zhengrong Xiang

引用次数: 0

A Velocity-Free and Memoryless PSO for Extreme Learning Machine-Based Deflection Basin Area Estimation in Industrial Cyber–Physical Pavement Monitoring 基于极限学习机的工业信息物理路面监测中挠度盆面积估计的无速度无记忆粒子群算法

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-06-08 DOI: 10.1109/TICPS.2026.3700587

Zhuoxuan Li;Jinde Cao;Shaosheng Xu;Jun Chen;Aleksander Byrski;Leszek Rutkowski;Wei Huang

{"title":"A Velocity-Free and Memoryless PSO for Extreme Learning Machine-Based Deflection Basin Area Estimation in Industrial Cyber–Physical Pavement Monitoring","authors":"Zhuoxuan Li;Jinde Cao;Shaosheng Xu;Jun Chen;Aleksander Byrski;Leszek Rutkowski;Wei Huang","doi":"10.1109/TICPS.2026.3700587","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3700587","url":null,"abstract":"Industrial cyber–physical systems (ICPS) for transportation infrastructure require accurate, data-driven condition assessment for intelligent monitoring and maintenance. Yet pavement responses under dynamic loading are highly nonlinear, causing conventional optimizers and learning models to stagnate in local optima. This study proposes two PSO variants—Inverse PSO (INV-PSO) and Chaotic Inverse PSO (CINV-PSO)—for robust global optimization. INV-PSO is supported by a convergence theorem and improves exploration via inverse updates, while CINV-PSO further introduces chaotic maps to enhance escape from local minima and maintain diversity. Both optimizers are used to train a lightweight Extreme Learning Machine (ELM) for predicting asphalt pavement deflection basin area from Falling Weight Deflectometer (FWD) measurements and related operational variables. Experiments show that INV-PSO-ELM and CINV-PSO-ELM outperform classical machine learning baselines and PSO-ELM in accuracy and stability. The proposed framework provides an efficient and practical cyber–physical solution that reduces the need for frequent on-site deflection testing while preserving high-fidelity pavement condition estimation.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"504-513"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148442209","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

Confidence-Aware Federated Learning for Smart Load Characterization in Cyber-Physical DER Systems 网络-物理DER系统中智能负载表征的自信感知联邦学习

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-07-09 DOI: 10.1109/TICPS.2026.3711591

Vishal Krishna Singh;Vins Patel;Neeraj Jain;Chhaya Singh;Rajkumar Singh Rathore;Weiwei Jiang

{"title":"Confidence-Aware Federated Learning for Smart Load Characterization in Cyber-Physical DER Systems","authors":"Vishal Krishna Singh;Vins Patel;Neeraj Jain;Chhaya Singh;Rajkumar Singh Rathore;Weiwei Jiang","doi":"10.1109/TICPS.2026.3711591","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3711591","url":null,"abstract":"The integration of smart meters into residential environments has allowed smooth collection of electricity consumption data, which is critical for demand response and coordinated operation in cyber-physical distributed energy resource systems. However, existing centralized methods of consumer characteristic identification pose significant risks to data privacy and confidentiality. Furthermore, the inherent limitations imposed due to noisy data cause a steep degradation in the accuracy and system-level decision-making. Addressing the issues of accuracy and data confidentiality, this paper proposes a confidence-aware federated learning framework for privacy-preserving inference of electricity consumer characteristics from raw smart meter data. The proposed method employs decentralized retention of smart meter data, using federated learning to refine network performance while ensuring that raw data remain localized at the client level. By evaluating a combined distribution of noisy and clean labels, erroneous data points are identified and excluded, thereby enhancing the model's efficacy and robustness. The effectiveness of the approach is validated using the Irish Commission for Energy Regulation dataset. The proposed framework demonstrates notable improvements, achieving an average gain of 3.97% in accuracy and 3.68% in MCC score compared to state-of-the-art methods, while maintaining strong data privacy guarantees.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"603-612"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148612990","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

Resilient Observer-Triggered Adaptive Control for Cyber-Physical Systems Under Time-Vary Stealthy FDI Attacks 时变隐形FDI攻击下网络物理系统的弹性观察者触发自适应控制

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-07-29 DOI: 10.1109/TICPS.2026.3717884

Lei Liu;Ruonan Ren;Baoling Miao

{"title":"Resilient Observer-Triggered Adaptive Control for Cyber-Physical Systems Under Time-Vary Stealthy FDI Attacks","authors":"Lei Liu;Ruonan Ren;Baoling Miao","doi":"10.1109/TICPS.2026.3717884","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3717884","url":null,"abstract":"Cyber-physical systems (CPSs) are widely used in safety-critical applications, where both control reliability and communication efficiency are essential. However, open networks make CPSs vulnerable to false data injection (FDI) attacks, which threaten system stability. Existing event-triggered control methods often fail to simultaneously ensure attack resilience, stability, and <inline-formula><tex-math>$H_infty$</tex-math></inline-formula> performance. This paper addresses the secure control problem of CPSs under FDI attacks by proposing an observer-based dynamic event-triggered control framework. To counteract the adversarial disturbances, a novel attack-resilient observer is designed to simultaneously estimate both the system states and the injected attack signals, enabling the synthesis of a secure observer-based controller. An advanced dynamic event-triggered mechanism (DETM) is developed by incorporating an internal dynamic variable, which adaptively adjusts triggering thresholds to significantly reduce communication frequency while avoiding Zeno behavior. Through Lyapunov-Razumikhin analysis, the closed-loop system is proven to achieve asymptotic stability and guaranteed <inline-formula><tex-math>$H_infty$</tex-math></inline-formula> performance, ensuring robustness against bounded FDI attacks. Theoretical results are validated via numerical simulations, demonstrating the effectiveness of the proposed method in mitigating attack impacts and conserving network resources.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"741-750"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148781109","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

Cooperative Sensor Scheduling and Optimization for Set-Membership Filtering in Industrial Cyber-Physical Systems 工业信息物理系统中集合隶属度滤波的协同传感器调度与优化

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-08-19 DOI: 10.1109/TICPS.2026.3725620

Yuru Guo;Zhongming Chen;Hui Peng;Jun-Yi Li;Yong Xu

引用次数: 0

Energy Management of AI Data Center Microgrids Considering Workload Uncertainties: An Online Decision-Focused Learning Method 考虑工作负载不确定性的人工智能数据中心微电网能量管理：一种在线决策学习方法

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-09-15 DOI: 10.1109/TICPS.2026.3733880

Jingxiang Wang;Qianwen Xu;Mingshen Wang;Bo Yang;Zhaojian Wang

{"title":"Energy Management of AI Data Center Microgrids Considering Workload Uncertainties: An Online Decision-Focused Learning Method","authors":"Jingxiang Wang;Qianwen Xu;Mingshen Wang;Bo Yang;Zhaojian Wang","doi":"10.1109/TICPS.2026.3733880","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3733880","url":null,"abstract":"With the rapid growth of artificial intelligence (AI) data centers (AIDCs), AIDC microgrids can participate in demand response by adjusting workload processing. However, AIDC workloads are highly stochastic, which poses challenges to online energy management. Existing management methods usually follow a predict-then-optimize (PTO) paradigm, which treats workload prediction and online optimization as separate tasks. Since perfect prediction does not exist, even small prediction errors may lead to large decision losses, thereby increasing the operating cost. To this end, this paper proposes an online decision-focused learning (O-DFL) algorithm for AIDC microgrid energy management under workload uncertainties. The proposed O-DFL framework integrates a workload prediction layer and a rolling-horizon dispatch optimization layer into a learning pipeline. Different from PTO methods, O-DFL updates the prediction model according to the decision loss, which improves online decision quality. Furthermore, an adaptive <inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula>-based decision loss detection module and an adaptive experience replay mechanism are developed to enhance online adaptability under dynamic workload conditions. Finally, the proposed O-DFL algorithm is validated in a Nordic energy market setting using workload data from a real graphics processing unit (GPU) cluster. Compared with the benchmark methods, O-DFL shows superior decision quality and adaptability to rapid workload changes. Over the 60-day online operation, O-DFL achieves an average daily decision loss of 22.13 kEUR/day, representing a reduction of at least 49.45% compared with the benchmark methods.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"1049-1062"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130718","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

A Consensus-Based Packet Loss Mitigation Strategy for Reactive Power Allocation of Parallel VSG Inverters 基于共识的并联VSG逆变器无功分配丢包缓解策略

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-08-25 DOI: 10.1109/TICPS.2026.3727419

Dezhi Xu;Jiaqi Wang;Jinlei Pei;Chengxi Zhang;Zhicheng Zhao;Yongwei Li

引用次数: 0

Stackelberg Game-Based Adaptive Impedance Control of Cyber-Physical Robotic Manipulators 基于Stackelberg博弈的网络物理机械臂自适应阻抗控制

IF 7.4

IEEE Transactions on Industrial Cyber-Physical Systems Pub Date : 2026-01-01 Epub Date: 2026-09-07 DOI: 10.1109/TICPS.2026.3731123

Ziyun Wang;Jiapeng Liu;Qing-Guo Wang;Jinpeng Yu

{"title":"Stackelberg Game-Based Adaptive Impedance Control of Cyber-Physical Robotic Manipulators","authors":"Ziyun Wang;Jiapeng Liu;Qing-Guo Wang;Jinpeng Yu","doi":"10.1109/TICPS.2026.3731123","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3731123","url":null,"abstract":"An adaptive impedance control policy based on Stackelberg game theory is proposed, for force and position tracking when the end-effector of a cyber-physical robotic manipulator contacts an unknown surface. The interaction process of the cyber-physical robotic manipulator with an unknown surface via closed-loop force feedback can be regarded as a Stackelberg game. An optimized equilibrium for force and position tracking errors and relaxed force constraint is achieved, and impedance parameters are dynamically updated. With the aid of impedance model, force tracking is transformed into position tracking problem. Then a tracking controller combining command filtered backstepping and barrier Lyapunov function is designed to ensure that the position tracking errors are uniformly ultimately bounded while the states remain within the safety boundary. In addition, by constructing an auxiliary system, the effect of input saturation is overcome. Stability analysis proves that all signals in a closed-loop system is uniformly ultimately bounded, and impedance parameters converge exponentially to a neighborhood of the Stackelberg equilibrium. Finally, simulation results demonstrate that the proposed control policy guarantees desired position trajectory tracking and desired contact force tracking.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"997-1005"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130703","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