IEEE Transactions on Industrial Cyber-Physical Systems最新文献

{"title":"Consensus Tracking of Multi-Agent Systems With Packet Dropouts Over Capacity Constrained Channels via Impulsive Encoding–Decoding","authors":"Yufan Chen;Mengqi Xue;Yue Song","doi":"10.1109/TICPS.2026.3738753","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3738753","url":null,"abstract":"This paper investigates mean consensus tracking for discrete-time multi-agent systems (MASs) over communication channels with limited capacity and packet dropouts. An impulsive encoding–decoding mechanism is developed to reconstruct the relative information from finite codewords. Bernoulli packet dropouts are incorporated into the transmission process, making the decoder correction unavailable whenever a transmission fails. To characterize the resulting decoder error, a mean detectability condition is established that explicitly reflects the effects of the quantization level, packet arrival probability, coding period, and error growth. Based on this condition, sufficient criteria are derived for consensus tracking analysis and controller gain design. Finally, a simulation example over a fixed directed leader–follower topology verifies the effectiveness of the proposed mechanism under packet dropouts.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"1084-1089"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149188338","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}

{"title":"Empirical Evaluation of AI-Assisted Risk Reasoning for ICPS Software Security","authors":"Padma Iyenghar","doi":"10.1109/TICPS.2026.3669084","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3669084","url":null,"abstract":"Operational Technology (OT) systems, which form the control and actuation layer of Industrial Cyber-Physical Systems (ICPS), integrate embedded control software in safety-critical environments where reliability and cybersecurity underpin overall system trustworthiness. Despite growing interest in Artificial Intelligence (AI)-assisted assessment, reproducible and standards-aligned benchmarks for evaluating cybersecurity risk reasoning in OT software remain limited. This paper introduces <italic>EmbSoftOTBench</i>, a standards-aligned benchmark that operationalizes IEC 62443-3-2 through machine-readable scenarios derived from MITRE EMB3D. The benchmark provides paired threat–benign scenarios with quantitative ILVE factors (Impact, Likelihood, Vulnerability, Exposure) and expert rationales, enabling systematic and reproducible evaluation of mitigation-aware cybersecurity risk reasoning rather than vulnerability detection alone. An experimental evaluation using state-of-the-art Large Language Models (LLMs) reveals near-chance classification accuracy on threat/benign discrimination, strong sensitivity to prompt structure, and systematic trade-offs between sensitivity, specificity, calibration, and auditability. These results indicate that current LLMs are not suitable for autonomous OT risk assessment but can support constrained, assistive, and audit-ready workflows. The dataset is publicly released under the CC BY 4.0 license to support reproducible research and standards-based validation in trustworthy ICPS environments.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"150-160"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147557791","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}

{"title":"Global Sensitivity Analysis for Cyber-Physical Power Distribution Network","authors":"Dongmeng Qiu;Suhan Zhang;Tianyu Luo;Xin Zhang","doi":"10.1109/TICPS.2026.3674974","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3674974","url":null,"abstract":"Cyber-physical power systems (CPPS) operate under uncertainties from both the power and communication layers, yet there are limited systematic methods to evaluate uncertain cyber-physical impacts. To address this gap, we build a CPPS model by coupling a power distribution network represented by differential–algebraic equations (DAEs) and a discrete-event communication network with node-specific communication delays. On this basis, global sensitivity analysis (GSA) is introduced to quantify the impact of cyber-physical uncertainties on the power distribution network, thereby identifying the most critical factors that affect CPPS operation. Probabilistic models are used to simulate load, photovoltaic (PV) generation, and communication delays, and a Monte Carlo framework is built to estimate Sobol’ sensitivity indices. Finally, a CPPS digital co-simulation platform is developed which integrates OPAL-RT power system simulator and OMNeT++ communication network simulator via shared memory mechanism to validate the CPPS framework. Case studies demonstrate the quantitative relationship between the sources of uncertainty and their impacts within the CPPS, indicating that PV generation dominates the variance of bus voltages, whereas communication node delays mainly affect local voltage stability in the test system.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"213-224"},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696207","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}

{"title":"Distributed Platooning Control of CAVs Under Practical Spacing Policies With Vehicular Data Privacy Protection","authors":"Shunyuan Xiao;Minglian Pan;Lei Ding;Maojiao Ye;Zhao-Qing Liu","doi":"10.1109/TICPS.2026.3712306","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3712306","url":null,"abstract":"This paper investigates distributed platooning control of connected and autonomous vehicles (CAVs) under practical spacing policies with vehicular data privacy protection. A practical spacing policy is first designed to adaptively regulate inter-vehicle distances by incorporating real-time traffic flow variations, road friction conditions, and safety distance requirements. The resulting spacing-policy-induced uncertainties are then addressed through an augmented system-based estimation approach. To safeguard vehicular privacy, a differential privacy-enhanced encoding-decoding mechanism is developed, enabling protection of both initial vehicular state information and broadcast vehicular data against eavesdropping attacks. Upon that, a co-design strategy is further proposed to jointly synthesize the privacy protection scheme, estimator, and distributed controller, ensuring reliable platoon tracking performance with data confidentiality. Finally, comparative case studies are conducted to validate the enhanced platoon tracking stability, driving safety, and privacy preservation in realistic traffic scenarios.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"698-707"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613007","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}

{"title":"A Physics-Informed Neural Network Framework Leveraging Deterministic Learning and Statistical Constraints for Bearing Fault Diagnosis","authors":"Peng Zhang;Qian Wang;Fukai Zhang;Danlu Wang","doi":"10.1109/TICPS.2026.3716614","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3716614","url":null,"abstract":"Recently, physics-informed learning-based intelligent bearing fault diagnosis methods have demonstrated great potential in improving diagnostic accuracy and physical consistency. Nevertheless, the practical deployment of conventional physics-informed neural networks typically relies on explicit partial differential equations (PDEs), which are generally difficult to acquire in real industrial scenarios. To address this limitation, a PDE-free physics-informed bearing fault diagnosis framework is proposed based on deterministic learning and statistical constraints. In the proposed framework, deterministic learning is adopted to extract nonlinear dynamic trajectories from multi-directional bearing vibration signals. The residuals between sample dynamic trajectories and class-specific standard trajectories are further converted into probabilistic statistical soft constraints. Accordingly, the system evolution mechanism embedded with physical dynamic characteristics is incorporated into the model training process, eliminating the need for explicit PDE constraints. In addition, a composite loss function is constructed by integrating the supervised classification loss with dynamic trajectory-based statistical constraint terms. This enables the network optimization process to be jointly guided by classification error and physical dynamic consistency. Finally, the effectiveness of the proposed method is validated through simulation and experimental studies. The results reveal that the average discernibility of dynamic trajectories reaches 0.3614, representing respective improvements of 122% and 68.6% over time-domain and frequency-domain signals. The proposed method achieves an effective balance among diagnostic accuracy, inference speed, and model complexity, with its average parameter volume reduced by approximately 93.9% relative to the comparative methods. These results demonstrate that the proposed method is applicable to bearing fault diagnosis scenarios where explicit PDE information is unavailable.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"719-730"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148746869","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}

{"title":"DP-Twin: A Data–Physics-Driven Method for Modeling Physical Twins of Industrial Cyber-Physical Systems","authors":"Jingzheng Mao;Yanbin Sun;Mohan li;Hao Liu;Zhihong Tian","doi":"10.1109/TICPS.2026.3679859","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3679859","url":null,"abstract":"With the increasing prevalence of sophisticated and stealthy cyber–physical attacks such as covert sensor tampering in industrial cyber-physical systems (ICPS), high-precision physical process modeling has become essential for accurate anomaly detection and other critical security defense tasks. To address this challenge, this paper proposes a data-physics–driven physical twin (DP-Twin) modeling that integrates partial differential equation–based physical constraints with physics-informed neural networks (PINNs), enabling a unified framework that fuses physical priors with data-driven learning. A shared temporal feature extractor is employed to capture common dynamics across multiple operating conditions, while lightweight PINN branches concurrently predict physical states and their derivatives. Furthermore, transfer learning is used to fine-tune the models, thereby reducing training costs and enhancing model generalization capability. When deployed for security monitoring in water treatment systems, DP-Twin leverages physics-consistency constraints to accurately detect abnormal behaviors. Experimental results demonstrate that DP-Twin maintains high predictive accuracy, exhibits strong robustness against noisy measurements, and provides reliable performance under out-of-distribution scenarios. In an industrial anomaly detection task, DP-Twin achieves a precision of 96.21%, highlighting its practical potential for ICPS security monitoring.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"246-258"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479302","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}

{"title":"A Synchrophasor State Estimator Algorithm That Guarantees Immunity Against GPS Spoofing","authors":"Mahesh Raju Badugu;Prashant V. Navalkar;Shreevardhan A. Soman","doi":"10.1109/TICPS.2026.3706643","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3706643","url":null,"abstract":"Power system state estimation using Phasor Measurement Units (PMUs) is a core functionality in the energy control centers executed with milliseconds granularity. Its results can be compromised by GPS spoofing of the PMUs. This paper proposes a novel synchrophasor state estimator algorithm that is immune to GPS spoofing. This guarantee holds so long as less than 50% installed PMUs are spoofed. The average runtime complexity of the proposed algorithm is the same as that of the linear synchrophasor state estimator, while its worst-case complexity is almost equal to that of a traditional nonlinear state estimator. Hence, it is very efficient. It is also highly sensitive i.e., it can detect sub-degree-level spoofing attacks. As a byproduct, it also identifies the spoofed PMUs and the quantum of spoofing without the overhead of any additional optimization or state estimation runs. Case studies and comparative evaluations on the IEEE-30 bus and the IEEE-118 bus system demonstrate the validity of the claims made.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"550-561"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479656","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}

{"title":"Distributed Zonotopic Fusion Estimation for Interconnected Systems With Communication Constraints","authors":"Xiaodong Zhang;Bo Chen;Rusheng Wang;Yucheng Zhang;Jianzheng Wang","doi":"10.1109/TICPS.2026.3723721","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3723721","url":null,"abstract":"This paper investigates the distributed zonotopic fusion estimation problem for large-scale interconnected systems subject to unknown-but-bounded noise and communication constraints. To cope with limited resources in sensor-to-local-remote estimator (S-LRE) channels, an event-triggered coding–decoding mechanism under bit rate constraints is introduced to reduce communication load. To exploit overlapping state redundancy induced by system decomposition, a zonotopic fusion criterion is proposed to yield fused estimates that are superior to local estimation. With the zonotopic interval width selected as the performance index, the estimator gain matrix and the fusion parameter matrix can be efficiently computed via parallel linear programming problems, and the optimal bit rate allocation for each S-LRE channel is obtained through offline optimization. Moreover, robust stability conditions are derived to guarantee the ultimate boundedness of the maximum interval width of the local zonotopic generator matrices. Finally, a system of four interconnected mass–spring–damper vehicles is employed to validate the effectiveness and superiority of the proposed methods.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"804-815"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148925398","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}

{"title":"A Cyber-Physical Framework for Drone-Based Mobile Energy Storage: Spatio-Temporal Optimization for Resilience and Lifecycle Viability","authors":"Puxiang Tan;Xinjiang Chen;Jianxiao Wang;Guannan He","doi":"10.1109/TICPS.2026.3724577","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3724577","url":null,"abstract":"Energy supply risks are growing, driven by the massive integration of intermittent renewable energy and the more frequent extreme events under climate change. We propose a decision model for drone-based portable energy storage systems (DPESS), conceptualizing them as highly flexible, topology-independent Cyber-Physical Distributed Energy Resources (DERs). We develop optimization models incorporating energy and transportation constraints for UAVs. Experimental results show that, in grid-connected economic scheduling applications (i.e., energy arbitrage), by dynamically dispatching energy storage units across nodes with price differences to arbitrage, DPESS significantly mitigates the asset idleness. In emergency support, DPESS could reduce load shedding losses by over 55% compared to trucks, covering 100% of critical nodes with an average response time of 2.1 hours (versus 8.7 hours for trucks). This work provides a theoretical foundation and technical roadmap for UAV applications in enhancing grid flexibility and disaster resilience.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"899-906"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148985717","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}

{"title":"RL-Driven Self-Triggered Optimal Stabilization for Unknown Nonlinear Networked Industrial Systems: An Adaptive Fuzzy Identification Design","authors":"Jian Liu;Jingjing Xia;Lijuan Zha;Jinliang Liu;Engang Tian","doi":"10.1109/TICPS.2026.3721794","DOIUrl":"https://doi.org/10.1109/TICPS.2026.3721794","url":null,"abstract":"This article addresses the optimal stabilization of unknown nonlinear networked industrial systems (NISs) with limited communication resources, proposing an innovative self-triggered approximate optimal control framework fused with generalized fuzzy hyperbolic model (GFHM) and reinforcement learning (RL) gradient descent. To handle unknown dynamics without prior model information, a GFHM-based state identifier is constructed, leveraging its universal approximation, flexible structure, and fewer parameters to adaptively reconstruct nonlinear dynamics. A self-triggered mechanism predicts the next control update instant via software, eliminating continuous hardware monitoring and reducing online computing cost, network data transmission overhead, and actuator energy usage. Within the RL framework, a critic neural network (NN) is established, with weights tuned via gradient descent to approximate the Hamilton-Jacobi-Bellman (HJB) equation solution and derive the approximate optimal control policy. Theoretical analysis proves the closed-loop signals are uniformly ultimately bounded (UUB). Numerical simulations validate the scheme's effectiveness in ensuring optimal control performance while saving resources.","PeriodicalId":100640,"journal":{"name":"IEEE Transactions on Industrial Cyber-Physical Systems","volume":"4 ","pages":"783-793"},"PeriodicalIF":7.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836383","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}